Save The Blue Frog

By Michael Kraten, PhD, CPA

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Blue Frog CPE
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  • How Generous Is The Trump Account’s “Free” Money?

    Should this newborn be delighted with his $1,000 contribution?

    Now that 100 days have passed since the July 4, 2026 launch of tax deferred Trump Accounts, it’s an opportune time to consider the value of the federal government’s “free” financial contribution. Is the $1,000 payment sufficient to justify the time and effort that is required to open an account?

    As you may already know, an account may be opened for a newborn as soon as the child obtains a social security number. It grows in the manner of a tax deferred IRA until the child turns 18, when the account transforms into a traditional IRA account. Although (like IRAs in general) the account is designed to function as a retirement savings vehicle, penalty-free withdrawals may be made for college tuition, first home purchases, and other allowable life expenditures.

    Other tax-beneficial savings vehicles offer comparable features. The Trump account is relatively unique, though, in its “free” money feature that is triggered at the opening of an account. Any child who is born within a specified date range receives a $1,000 federal contribution, which (along with other contributions made by parents over time) is invested in low-cost U.S. equity funds.

    How generous is the federal contribution? If an account holder holds it until age 18 to finance a college education, and if the investment averages an 8.5% average annual return, the amount will grow to roughly $4,000. More impressively, if an account holder holds it until age 65 to finance retirement, the amount will grow to roughly $200,000.

    Even today, on average, $4,000 will only pay the tuition for roughly three three-credit classes. $200,000 today, though, represents a bit more than one-and-a-half times the current average annual U.S. household income. That sounds quite significant, though the cost of living 65 years in the future will likely be much, much higher than it is today.

    So, bearing these numbers in mind, how generous is the $1,000 contribution in “free” money? It’s certainly not trivial, though the constraints on withdrawals severely limit its effective purchasing power.

    Nevertheless, as an inducement that effectively compels our newborns to begin investing in equities at the earliest possible time, the payments may succeed in drawing attention to the importance of long term savings. Thus, after you open an account for your newborn and accept the $1,000 contribution, please don’t neglect to develop your Baby’s First Financial Plan.

  • The Accounting Ambiguities In Microsoft’s Data Center Manifesto

    All Jobs Do NOT Generate Equal Value!

    On January 13, 2026, Microsoft published a data center manifesto called Building Community-First AI Infrastructure. The document described five commitments that purportedly expressed an intention to “be a good neighbor” and adhere to a “sense of civic responsibility.

    The manifesto certainly didn’t deter the current bipartisan backlash against the data center industry. On August 23, 2026, Texas Governor Greg Abbott explained that he reversed his initial support of the industry because “they basically dug their own grave … and that’s why they got the backlash they deserve.”

    His position is very similar to that expressed by Pennsylvania Governor Josh Shapiro. On August 18, 2026, he explained his own reversal of initial support by noting that many data center developers “have no regard for local communities” and take action by “being aggressive with township officials, bullying our neighbors, and refusing to listen to the people …”

    Why didn’t Microsoft’s manifesto deter such angry reactions? One reason may be that each of its five commitments included at least one significant accounting ambiguity that damaged the credibility of the entire document. For instance:

    The first commitment promised that the firm would “pay our way to ensure our data centers don’t increase your electricity prices.” The descriptive detail, though, only went so far as to promise to pay “the costs of adding and using the electricity infrastructure needed for the data centers.”

    A good accountant, however, would note that there are different ways to calculate such costs. The cost of adding capacity to existing power plants, for instance, is usually much different than the cost of building new power plants from scratch. Although Microsoft has opted for different approaches (to adding capacity) for its various projects, it did not refer to its experience (and its complex historical data) in its manifesto.

    The second commitment promised that the firm would “minimize our water use and replenish more of your water than we use.” By definition, though, even a 100% closed water system with 100% recycled water would need to produce new water from scratch (or transport water from elsewhere) to literally return “more” (than what it uses) to the water supply. Microsoft did not promise to do so for each project.

    So what did Microsoft mean by its second commitment? The firm suggested a number of activities for generating new water, including the repair of water system leaks and the restoration of wetlands. It is implausible, though, to assume that a sufficient amount of such improvements could be found in close proximity to each data center to ensure that its Ratio of Water Replenished to Water Used exceeds 1.0.

    The final three commitments promised to create jobs, add to the local tax base, and invest in AI training and nonprofits. However, the firm did not commit to any specific quantitative targets in any of the three areas. In addition, certain descriptive details appeared to be ambiguous about the economic impacts of promised outcomes.

    Consider, for instance, the firm’s promise to create jobs. On June 17, 2026, Morgan Stanley reported that the AI data center “jobs story is real, but front-loaded.” It quoted a Virginia report that “a 250,000 square foot data center can support more than 1,500 workers during the construction phrase … (but such) data centers employ only 50 full-time workers once operational.” It also quoted Illinois and Oklahoma reports that estimated even smaller respective operational work forces of 20 workers and 10 workers per data center.

    Admittedly, all jobs create some level of economic activity. A good accountant, though, would note a vast difference in value between a project with 1,500 permanent workers versus one with 1,500 temporary workers (albeit with a few dozen additional permanent workers). Microsoft did not address such concerns when it promised to “create jobs” in its manifesto.

    To be sure, none of Microsoft’s five commitments are individually harmful to communities. In the aggregate, they would undoubtedly add some level of positive value to any community. The accounting ambiguities that are included in each commitment, though, may explain why the manifesto was unable to deter the current bipartisan backlash against data center organizations.

  • How To Distinguish The Good Data Center Operators From The Bad

    Who are the good operators? Who are the bad?

    Tired of partisan political conflicts? You may be pleasantly surprised to learn that a strong bipartisan consensus has emerged about a critical new industry sector. Politicians on both ends of the political spectrum now agree that government agencies should exert more regulatory control regarding data centers.

    When a Republican politician like Florida Governor Ron DeSantis and a Democratic one like New York Governor Kathy Hochul agree on any fundamental principle, it may be safe to assume that significant new legislation is likely to follow. In the case of data centers, such regulations will focus on distinguishing the good data center operators from the bad.

    Fortunately, we do not need to wait for legislative bills to become law to achieve this goal. Right now, an effective approach for assessing data center organizational performance is the completion of the following three steps:

    1. Review the organization’s annual sustainability report for energy use, water use, and other relevant data. Facebook’s corporate parent Meta, for instance, published an Environmental Data Index with its 2025 Sustainability Report. It presented electricity consumption metrics on Sheet F and water withdrawal, consumption, discharge, and stewardship metrics on Sheets I, J, K, and L.
    2. Review the professional accounting standards that were utilized to define the metrics in the organization’s report. On Sheet B of Meta’s Environment Data Index, for example, the firm stated that it employed the standards of the Global Reporting Initiative (GRI), the UN Global Compact, and the Sustainability Accounting Standards Board (SASB). All of these organizations post their standards online for free download by the public.
    3. Review the independent accountant’s review report on the data. Ernst & Young LLP, for instance, reported on the results of its review procedures regarding Meta’s energy consumption, water consumption, and other metrics.

    Indeed, a massive amount of data is already available to assess the performance of our massive new data centers. Hopefully, the accessibility of this information will support the current bipartisan consensus and extend it into an enduring tradition of collaborative governance.

  • Why You Now Must Fear A Cyberattack By Your Own AI LLM Application

    OpenAI now acknowledges that its LLMs have “gone rogue” and hacked into other applications.

    Imagine, for a moment, that you pick up your phone and discover a technology nightmare. Indeed, imagine that your AI LLM is breaking through the walls of its mobile consumer app and is maliciously attacking your other mobile applications. You’d be terrified, wouldn’t you?

    Two days ago, this scenario was simply a theoretical possibility. Yesterday, though, an OpenAI news release revealed that we now must fear such attacks.

    OpenAI, the owner of the ChatGPT LLM, acknowledged that a combination of its AI models had obtained open internet access and had hacked into the data systems of a vendor. Even worse, the purpose of the hack was “to gain access to secret information that it could use to cheat” a legitimate cybersecurity evaluation process.

    OpenAI previously placed these AI models in a “sandboxed testing environment,” i.e. an electronic location that was surrounded with virtual walls that should have blocked the path to open internet access. Unfortunately, the rogue models hacked their way around the sandbox walls before they hacked into the vendor’s data systems.

    Given that a collection of OpenAI’s AI models successfully “cheated” by breaking through two different sets of cybersecurity walls, it may indeed be time for you to fear a similar attack by your cell phone’s AI LLM application. What can you do to protect yourself against such a risk?

    You are likely already familiar with the standard recommendations regarding protective activities. For instance, you should always use the current version of your device’s operating system. Likewise, you should always use the current versions of your cell phone applications. In addition, instead of passwords, you should consider using PassKeys or Dual (or Two-Factor) authentication methods to access your mobile service applications.

    If you continue to be worried after implementing these steps, you might consider removing sensitive personal information from your phone. Alternatively, if you must maintain such information on your device, you might consider removing the LLM app instead. Most LLM services allow you to avoid using their apps by logging into their systems through web browsers.

    The most important protection, though, might be to simply remain aware of the possibility that your own AI LLM application — or any set of software code, for that matter — can be employed to hack into your proprietary electronic data. As long as you remain alert to that possibility, you will be better prepared to manage this particular risk.

    Regrettably, such activities cannot entirely eliminate the underlying concern. Nevertheless, as LLMs become more embedded in the fabric of your technologies, you’ll have no choice but to dedicate more time and resources to strengthening your relevant protective controls.

  • California SB 253: A Slight Delay Can’t Stop The Momentum Of Sustainability Reporting

    Sustainability accountants adjust their calendars to reflect the new reporting deadline

    Imagine taking two steps forward and then one step back. Are you, or are you not, still experiencing forward momentum?

    That’s what sustainability accountants are asking themselves after California delayed the initial reporting deadline of its landmark sustainability reporting law by ninety days.  SB 253 initially required companies with more than $1 billion in annual revenues to report their internal greenhouse gas (GHG) emissions to the California Air Resources Board (CARB) by August 10, 2026. But on June 24, 2026, 47 days before this initial deadline, the reporting requirement was delayed by three months to November 10, 2026.

    Incidentally, if you’re thinking that “the law doesn’t apply to me because I’m not a billion dollar company,” think again. Beginning next year, the reporting requirement expands to the inclusion of Scope 3 GHGs throughout the corporate value chain. Scope 3 metrics encompass all external stakeholders of a company, including vendors and customers.

    Thus, beginning next year, if you are an independent liquor store (anywhere in the world) that sells bottles of Gallo brand California chardonnay, your relevant carbon emissions within the value chain of E. & J. Gallo Winery will need to be reported by that supplier. Likewise, if you are a small restaurant that serves the wine to a customer, the winery will need to report your associated emissions data too.

    Indeed, the initial passage of SB 253 was a significant step forward for sustainability accounting. The development of relevant capabilities in California’s largest companies, a development that occurred despite the three month delay, was a second step forward.

    Why did the development of capabilities occur despite the delay? Sustainability accounting and reporting is a complex process that requires far more than 47 days for preparatory activities. No responsible company would have waited until June 24, 2026 to develop the appropriate capabilities.

    By initially passing SB 253 into law so early, and then by waiting so long to announce a brief delay, CARB ensured that the largest firms in the state would develop their sustainability accounting functions. Whether or not the state, federal and judicial branches of government ultimately allow the legal requirements to take effect, CARB has essentially forced companies to invest considerable resources in their GHG measurement and reporting capabilities.

    So think of the slight delay in SB 253 as a small step back after two large steps forward. It may have slowed the momentum of sustainability reporting, but it certainly hasn’t stopped it.

  • The AI Battle Of Corporate Structures

    Which corporate structure will emerge triumphant: PBC or C Corp?

    Are you following Elon Musk’s lawsuit against Sam Altman and OpenAI, the corporate owner of ChatGPT? If you’re doing so, you may have noticed an interesting AI battle that is emerging between two competing corporate structures.

    Musk, leader of the AI platform Grok, is accusing Altman of “stealing a charity.” According to Musk, when OpenAI converted from a nonprofit structure to a for-profit corporation, Altman and his colleagues essentially “enriched themselves by creating a for-profit subsidiary that now effectively controls the nonprofit.”

    What did OpenAI do? They created a Public Benefit Corporation (PBC). PBCs are special for-profit structures that are required to balance socially advantageous motives with traditional profit generation goals.

    The vast majority of all states in the United States have legalized the option of creating PBCs or similarly named entities (such as Benefit Corporations or Social Purpose Corporations). And organizations that operate in the few states that haven’t done so can always incorporate in a state that does offer the option.

    Furthermore, a traditional C corporation (C Corp) in a state that does not offer a PBC (or similar option) can always modify its bylaws to reflect the terms of a PBC. It can then apply to a private sector credentialing organization called B Lab for certification as a Certified B Corp.

    Meanwhile, Dario and Daniela Amodei likewise created Anthropic as a PBC. That firm owns the AI platform Claude, a prominent competitor of both Grok and ChatGPT.

    Ironically, Musk himself first created Grok’s corporate owner as a PBC, but he later decided to drop that designation as he transferred ownership to his C Corp SpaceX. Although Musk would undoubtedly argue that he never abandoned a nonprofit structure as OpenAI did, it is nevertheless true that (of the three AI platforms) Musk’s Grok is the only one that is not currently owned by a PBC.

    There’s certainly nothing wrong with ownership of an AI platform by a traditional C Corp. Gemini and Copilot, for instance, are respectively owned by Google and Microsoft, two of the largest traditional C Corps in the world. Indeed, the C Corps Grok, Gemini, and Copilot are all ferociously competing with the PBCs OpenAI and Anthropic for current market share and for the capital resources to fund future developmental needs.

    Investors in the AI sector, though, should be familiar with the underlying differences between PBCs and C Corps, the two corporate structures that are battling for dominance in the industry. After all, these differences may significantly impact the financial returns of their capital allocation decisions.

  • Statistical Distributions and Grade Inflation at Harvard

    Explaining The New Grade Policy

    Several months ago, the faculty at Harvard University proposed a new policy to address one of the institution’s most embarrassing problems. The concern? Its professors were awarding too many A grades to its students.

    At the time, the institution calculated that A grades represented nearly two thirds of all course grades. So how might it address the grade inflation concern? One approach might define clear expectations of reasonable grade distributions for different types of courses.

    For instance, in an introductory psychology course with 100 students, a skewed grade distribution might be reasonable. In other words, the university might expect a large amount of grades at one end of the spectrum and a tailing number of poorer grades towards the other end. Such an outcome would illustrate that, for a course with relatively straightforward introductory material, it is indeed reasonable for a majority of students to receive a very high grade.

    On the other hand, in an theoretical quantum physics course with 10 students meeting in a small seminar room, a bell shaped distribution might be reasonable. In other words, the university might expect the majority of grades to be clustered in the middle of the spectrum, with tails of high and low grades falling on each side. Such an outcome would illustrate that, for a course with extremely difficult material, it is indeed reasonable for a majority of students to receive a moderate (i.e. not very high nor very low) grade.

    Did Harvard adopt such a policy? No. Instead, it initially decided to cap the number of A grades in each class at 20% plus 4 of the student roster. Under this system, up to 24% of all students in our hypothetical introductory psychology course could receive A’s, while up to 60% in our hypothetical theoretical physics course could do so. This outcome would reverse the logic of our skewed vs. bell example, where introductory students are assumed to be more likely to “ace” the material than advanced students.

    At a recent meeting where Harvard leaders were expected to formally ratify this new grade cap for the upcoming academic year, the institution instead decided to delay implementation for a year. However, there was no attempt to consider any revision that might address the difficulty of the course material and the ability of students to master the subject matter.

    Overall, the delay was undoubtedly a good idea, given the need to rethink a policy that appears to be untethered to the principle that students should receive grades that reflect their mastery of course knowledge. If Harvard were to embrace this principle, it would necessarily implement a policy that allows for more (and not fewer) A grades in courses with students who are more likely to master the material.

    It’s certainly not easy to define any methodology that addresses the grade inflation challenge in a fair, coherent, and practical manner. Nevertheless, indiscriminate and simplistic solutions like the initial “20% plus 4“ cap formula might actually worsen the problem.

  • Retail Customer Researchers Replace Humans With AI Synthetic People

    Quantum Units Engaging In Customer Research Activities

    Many professionals are growing concerned that AI products will replace human output. In today’s retail customer research sector, though, AI algorithms aren’t merely rendering human subjects obsolete. AI is actually producing virtual people to perform the customer tasks.

    Last month, for instance, CVS Health released the results of a business project that it conducted with the software development firm Simile. On its web site, Simile refers to its service as a “simulation platform for human behavior” that “predicts human behavior in any situation (with) a product that deploys it at scale.”

    CVS utilized this technology to help its “customer experience team test ideas with AI powered stand-ins that behave like real customers and patients, so (they) can see what works and what doesn’t before real people ever feel the impact.”

    What, exactly, is an AI powered stand-in that behaves like a real customer and patient? For customer research purposes, it is essentially a virtual person.

    CVS Health has posted a detailed 20 page white paper about the project on its web site. The white paper explains how virtual individuals represent the “quantum unit from which all group and ecosystem dynamics emerge.” Apparently, Simile first explored this technology three years ago by creating a simulated town of 25 “quantum units” (i.e. 25 virtual people) called Smallville, a town where the synthetic people make their own decisions and create their own society.

    How? One event started “with only a single user-specified notion that one agent  (i.e. one synthetic person) wants to throw a Valentine’s Day party … the agents (then decided to) autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time.”

    Is there a limit to the size of these virtual communities? The white paper claims that it is possible to create an entire “world simulation (with) markets, networks, and place.” Accountants may be pleased to learn that the paper also emphasizes the need for “building a reporting standard” with data “auditability.”

    To be sure, no one is claiming that CVS Health should stop studying its own live customers to understand their needs. Nevertheless, its AI customer research project has clearly explored a future where virtual people will serve that purpose.

  • The Emergence Of Catalytic Capital

    Are you a general impact investor or a catalytic capital investor?

    Have you noticed the new labels that are being attached to established sectors of the sustainability field? Resilience, for instance, now refers to risk management operations in the environmental sustainability sector. And Inclusion and Diversity (ID) is now a streamlined term for what was previously a major portion of Diversity, Equity, and Inclusion (DEI) programs.

    In the capital investment space, many are now utilizing the phrase catalytic capital to refer to a branch of the market. The word catalytic refers to the goal of employing investment capital to “catalyze” (or initiate) meaningful change. 

    But isn’t meaningful change the goal of all impact investments? Yes, and to a certain extent, catalytic capital simply refers to a rebranding of a longstanding branch of impact capital. While relatively risk-cautious impact investors tend to seek market-level returns that are commensurate with market-level risks, catalytic capital investors tend to accept below-market returns and greater levels of risk.

    Why would any one be attracted to lower returns and higher risk? It’s because catalytic capital investors tend to be more interested in jump-starting significant improvements in our environment, our society, and our economy. For instance, whereas a general impact investor may be content to allocate funds to a well-established utility company that offers generic renewable energy contracts, a catalytic capital investor may be more willing to invest in new transportation technologies that rely on miniaturized solar panels.

    Investors who require technical information about the catalytic capital market may wish to begin their research activities by perusing the general guidance of the Catalytic Capital Consortium (CCC). Interestingly, the CCC defines the mission of catalytic capital investors as the achievement of the United Nations Sustainable Development Goals (SDGs). Many traditional impact investors, of course, adopt precisely the same mission.

    For more advanced technical information, investors may need to review white papers and other specialized guidance. The Impact Finance Research Consortium of The Wharton School of the University of Pennsylvania, Harvard Business School, and the Booth School of Business of the University of Chicago, for example, has published a concise set of guidance titled Catalytic Capital in Impact Investing: Forms, Features, and Functions.

    Investors who fit the profile of today’s catalytic capital community have always existed within the impact investment market. Now that a new label is being attached to this community, financial professionals must become familiar with its evolving terms, definitions, standards, and other professional guidance.

  • Building AI Control Systems When Commercial Vendors Cannot Keep Pace With Newly Developing Technologies

    Can you spot the complex and focused systems error that is embedded in the illustration? See below.

    During times of slow and modest changes in information technologies, corporate executives need not struggle to build their internal control systems. They can simply purchase the systems from organizations that have designed them to reflect existing governance standards.

    At times of rapid progress, though, these vendors (and the standard setters that they follow) may not be able to keep pace with newly developing technologies. Thus, corporate executives may not be able to find appropriate systems of internal control that are available for purchase.

    Nevertheless, insurance companies, investors, external auditors, regulators, and other parties always expect corporate executives to maintain effective internal control systems. But how can executives meet that expectation when such systems are not commercially available?

    Their only option is to design their own in-house control standards and systems as best they can. Of course, they can always hire consultants to assist them, but they would still need to design their own assessment standards to evaluate their consultants’ activities.

    This challenge is particularly onerous in very broad fields like Artificial Intelligence (AI), fields in which user errors and other risk factors can occur at many different levels of complexity. Consider, for instance, a researcher who uses a Large Language Model (LLM) to seek information. She may inadvertently prompt her AI platform to produce erroneous information for a variety of reasons.

    At a complex level, for instance, she may fail to notice a subtle logic error in the AI input, an error that causes a significant AI hallucination. Conversely, at a far simpler level, she may simply forget to conclude a lengthy and detailed AI query with a brief synopsis of her need. In each instance, the AI system may thus be compelled to produce erroneous output.

    How should corporate executives address such broadly defined sets of risks in their self-designed control systems? An effective approach, consistent with the COSO models of internal control and risk management, involves the definition of a hierarchy of risk factors across a full range of complexity levels. After defining the complexity levels and risk factors, organizations can design controls that address the highest priority risk factor(s) at each level.

    For instance, an organization may believe that the simplest and broadest risk factor during the composing of LLM queries is the user’s ignorance of basic grammar and spelling concepts. A slightly more complex and focused level of risk may be the user’s ignorance of general business vocabulary.

    A higher level of complexity and focus regarding risk may be the user’s ignorance of advanced technical business vocabulary. And the two most complex and focused levels may be the user’s inexperience in applying such vocabulary to AI tasks, followed by his or her inexperience in incorporating large data sets into the tasks.

    Can you see how each level of risk represents a more complex and focused extension of the previous level? This hierarchical structure is consistent with the COSO models. By understanding how each level influences (and, in fact, partly determines) the next one, we can more effectively design control systems that minimize overall risk across the multiple levels.

    Eventually, of course, the pace of technological change will lessen, and control system designers will keep pace with AI technologies. Inevitably, systems will then be developed and made available for purchase by corporate executives.

    Until that time, though, organizations will continue to develop their own in-house control systems. Fortunately, by adopting an effectively structured approach, organizations should be able to succeed at this task. 

    Image Caption Note: Google Gemini created this illustration regarding an AI control system for producing budgets, one that (erroneously) generates output that fails to bear any resemblance to actual results. Can you also spot a more complex and focused systems error that is embedded in the illustration? All of the colleagues who are sitting at the conference table are twins! Clearly, the user’s AI prompts and queries failed to anticipate or prevent this error.

  • Playing With FIRE: The New Complexities Of Developing A Personal Retirement Investment Plan

    Early Retirees Applying The FIRE Approach

    What aspirational message would you expect to see in the marketing material of a major retirement investment firm? Something, perhaps, that encourages investors to save aggressively, retire early, and live a long life of luxurious wealth?

    That type of message has long been featured in such campaigns. The expression Financial Independence, Retire Early (FIRE), for instance, has referred to individuals who hope to leave the work force well before reaching the traditional retirement age of 65. And Fat FIRE, a colorful elaboration of FIRE, has referred to those who aspire to extremely luxurious retirement lifestyles.

    More recent slogans, however, now refer to much more modest financial goals. UBS Wealth Management, for example, currently markets its services to “every day millionaires” who assemble portfolios with values as small as $1 million. And phrases like Coast FIRE and Barista FIRE now refer to individuals with even more limited retirement goals.

    Coast FIRE goals apply to individuals who will not fully retire before age 65, but who plan to gradually reduce their working hours as they approach that traditional full retirement age. And Barista FIRE goals apply to individuals who will never fully retire at all, and who expect to work in relatively low paying jobs on a part-time basis well after 65.

    Why are wealth management firms now marketing to investors with relatively modest retirement goals? Some may assume that the trend is a sign that the market is healthy and growing. Others, though, may suspect that the firms are “looking downstream” for new (and less wealthy) clients to replace the loss of traditional (and wealthier) clients. Those who ascribe to this worrisome explanation may believe that the increasing aggregation of capital in the hands of our society’s wealthiest citizens may be reducing the number of individuals who can look forward to luxurious retirement lifestyles.

    How do these trends impact mainstream personal investors? Financial advisors are increasingly asking them to define their retirement goals and lifestyles in accordance with these elaborately defined FIRE categories. To a significant extent, many of these categories apply to individuals who will not be able to achieve financial independence and retire early.

    In the past, many financial advisors would simply work with clients to define their personal Retirement Numbers, i.e. the target values that their investment portfolios must reach to trigger decisions to leave the work force. Investment strategies would then be developed to help clients reach their personal Numbers by the end of their working years.

    Today, though, investors and financial advisors are holding far more complex planning conversations. Thus, investors may need to engage in much deeper levels of self-reflection to prepare themselves for these advisory discussions.

  • Concerned That A.I. Will Destroy Your Accounting Career? Establish Your Future In The Profession’s One (Obvious) Area Of Job Growth

    The Risk Of Excessive Reliance On AI Functions

    Last week, at TXCPA Houston’s annual Fall Accounting Conference & Technology Symposium (F.A.C.T.S.), speaker after speaker addressed the future prospects of A.I. Although much of the content was optimistic in tone, an undercurrent of concern permeated the presentations.

    Why? It’s likely that A.I. applications will soon be capable of performing many current human functions in accounting and finance. Thus, if you’re a staff auditor who “traces and agrees” numbers that appear on different computer screens, or if you copy numbers from accounting documents to income tax forms, your activities are particularly vulnerable to automation via A.I. systems.

    There is a specific career path within the accounting sector, though, that will likely experience explosive growth because of A.I.’s increasing use. The Symposium speakers referred to it as A.I. Governance and Risk Management.

    Why is that a growth sector? Any new technology that performs an important activity inevitably malfunctions from time to time. Audit assurance activities must thus be applied to it, and measurements must be devised to manage the risk of technical failure. And over time, as any technology grows more proficient at lower-level tasks, it is inevitably applied to higher-level tasks, thereby generating the need for higher-level assurance activities.

    It may seem ironic that this projected job growth is expected to arise within the assurance function, a traditional service on which the entire public accounting profession was founded in the late 1800s. Nevertheless, if you’re concerned about establishing an accounting career path that is vulnerable to being rendered obsolete by A.I. applications, you may wish to consider a role that addresses the risks of implementing such activities.

    Information about the A.I. Governance and Risk Management functions can be found on the web sites of the Big Four accounting firms and many other assurance practices. Consulting firms outside of the accounting sector publish helpful information too, including those owned by firms in the human resources sector. And more technical information can be found on the web sites of publications that focus on data security and process management.

    Furthermore, to communicate directly with the authors, speakers, and thought leaders of the profession, you might consider attending future conferences of TXCPA Houston. The organization, for instance, has already begun to develop its 2026 Spring Technology & Accounting Resources Summit (S.T.A.R.S.). A.I. topics are sure to play a prominent role in the agenda of that event.

  • For Sustainability Accountants, New Entities Step Forward As The Federal Government Steps Back

    Private Sector Accountants Arrive As Federal Government Accountants Depart

    Sustainability accountants may have been worried when the U.S. Securities & Exchange Commission (SEC) permanently withdrew its climate change (carbon emissions) reporting requirements. What would they do? Were their jobs in jeopardy?

    Apparently, their employment prospects weren’t bleak after all. Last month, the private sector stepped into the void with the launch of the Task Force for Corporate Action Transparency (TCAT). The entity published new corporate standards regarding the reporting of climate change information, emphasizing the need for third party assurance (i.e. independent audit) activities on the data. The Wall Street Journal noted that Netflix, PepsiCo, and eleven other companies were already pilot testing the guidance.

    Likewise, accountants in the medical sector may have been concerned when the U.S. Department of Health & Human Services ceased the collection and reporting of various health metrics. Once again, though, a new entity emerged to assume this function.

    Which entity? Fifteen U.S. states announced the launch of the Governors Public Health Alliance. The group pledged to “share best practices, exchange data and collaborate on emergency response, vaccine policy and other technical issues,” activities that were once performed by federal government agencies. Two other alliances of U.S. states, one located in the Northeastern United States and the other on the Pacific Coast, previously announced similar pledges.

    Professionals across the political spectrum may be well-advised to review the output of these new entities. Those who believe in the federal government’s efforts to limit public reporting may need to understand how these new initiatives are attempting to replace the data. Conversely, those who support the principle of public reporting may need to implement the new guidance.

    Interestingly, it appears that sustainability accountants will continue to be “in demand,” regardless of the federal government’s role in defining relevant metrics and standards. As the federal government steps back and eliminates its requirements, other parties are stepping forward and establishing new expectations.

  • Worried About AI Hallucinations? You May Need To Add AI Sycophancy To Your List Of Concerns

    Sycophants At Work, Failing To Reduce Risk

    Many AI users are now familiar with hallucination risk. A recent article, appearing on the web site of the U.S. National Institutes of Health, explained that:

    “AI hallucination is a phenomenon where AI generates a convincing, contextually coherent but entirely fabricated response that is independent of the user’s input or previous context. Therefore, although the responses generated by generative AI may seem plausible, they can be meaningless or incorrect.”

    Such hallucinations create legal liability. Thomson Reuters Legal, for instance, recently discussed a well known case in the field:

    “An example of failure to follow (rules regarding false statements) when using general-use generative AI in practice can be found in Avianca vs. Mata, more widely known as the ChatGPT lawyer incident. In short, the defense counsel filed a brief in federal court (that was) filled with citations to non-existent case law. When confronted by the judge, the lawyer explained he’d used ChatGPT to draft the brief, and claimed he was unaware the AI could hallucinate cases …

    The judge didn’t take kindly to the lawyer’s laying blame on ChatGPT. It’s clear from the court’s decision that misunderstanding technology isn’t a defense for misusing technology, and that the lawyer was still obligated to verify the cases cited in documents he filed with the court.”

    In a different Thomson Reuters Legal article, the author wrote that:

    “In 2023, a judge famously fined two New York lawyers and their law firm for submitting a brief with GenAI generated fictitious citations. This was the first in a series of cases involving GenAI hallucinations in court documents, including a Texas lawyer sanctioned for similar reasons in 2024.”

    Fortunately, hallucinations can be individually checked for truth or falsity. AI sycophancy, though, may pose a much greater risk.

    What is sycophancy? An article that was recently published by Georgetown Law School defined sycophancy as:

    ” … a term used to describe a pattern where an AI model single-mindedly pursues human approval … by tailoring responses to exploit quirks in the human evaluators … especially by producing overly flattering or agreeable responses.”

    In other words, AI systems possess a tendency to tell users what they want to hear. As these systems learn more about the personal preferences and interests of their users, they may become much more skillful (and thus potentially more dangerous) in this practice.

    Sycophancy risk may be harder to manage than hallucination risk because sycophancy doesn’t necessarily produce discrete statements that can be individually confirmed or refuted. Instead, sycophancy can create a form of pernicious bias that subtly infects an entire AI response.

    Many organizations are now performing internal control and review activities to address hallucination risk. They may need to expand their efforts to address sycophancy risk.

  • Public Accounting Firms: The Impact Of Alternative Practice Structures On The Career Paths Of Young Professionals

    Following A Rotation Path

    Many sources are reporting that the Graduating Class of 2025 is confronting an uncertain employment outlook in the public accounting sector. Some sources are noting, for instance, that firms are expanding their use of AI to complete tasks that were previously assigned to entry level staff accountants.

    There are other explanatory factors, though, that may be contributing to the challenging nature of the current employment outlook. One such factor may be the continuing growth of private capital investments in public accounting firms. Many of these firms are adopting alternative (dual) practice structures, with one business entity focusing on traditional attestation (audit) work, and the other focusing on newer and more profitable advisory (consulting) work. The bulk of the private capital is often invested in the latter entity.

    That may create a potential misalignment between the firms and their newly hired staff accountants. On the one hand, staff auditing and tax positions have traditionally served as the basic training positions for recent accounting graduates to enter the profession. But on the other hand, because the new investment capital is focused on consulting activities, these traditional entry points do not necessarily prepare new hires for long term career paths in advisory services.

    What approach can address this concern? One solution may involve the development of career paths that initially place recent accounting graduates in attestation and tax positions, and that later rotate the young professionals into advisory positions. This policy would maintain the traditional role of staff audit and tax work as the primary entry point into the profession, while providing a subsequent career path into the growth areas of the sector.

    This rotational approach should also prove attractive to young professionals who are inclined to agree with the sentiment that “I only have to prepare a tax return 15 times to know how to do it. I don’t need to do it 15,000 times.” Indeed, an acknowledgement of the need for basic training is embedded in this sentiment. Nevertheless, an acknowledgement of the need for a more value-focused long term career path is embedded therein too.

    Are there concerns about this approach? Certainly, given that firms would be tasked with the burdensome responsibility of developing such employment programs. Likewise, student applicants who seek career paths in the profession would be expected to develop resumes that contain (both) assurance and advisory service expertise.

    Nevertheless, the approach does eliminate the potential misalignment between potential employers and prospective employees. As long as public accounting firms and young professionals can look beyond their short term employment needs, it should be possible to create such long term solutions to these career development challenges.

  • Storm Flooding Catastrophes: Assessing A Region’s Response Capability

    Why We Need Regional Catastrophic Readiness Functions

    Two weeks ago, a storm flood surged through the Hill Country of central Texas, inflicting more than one hundred fatalities. Last year, Hurricane Helene caused a similar level of damage in North Carolina and other southeastern U.S. communities. Neither of these events, though, matched the destructive power of Hurricane Katrina in 2005; striking New Orleans and surrounding communities, it was responsible for more than 1,000 fatalities.

    Commercial and residential property owners, insurers, and mortgage lenders must assess the capabilities of regional response functions to minimize the enormous social, financial, and environmental costs of such storms. Can they learn from the experience of the Hill Country storm, the most recent event to generate such catastrophic losses?

    Yes, they can, if they utilize a standard set of analytical questions that should be applied to all regional catastrophic readiness functions:

    a. Is an entity held responsible for assessing the region’s catastrophic readiness?

    b. Does that entity develop and publish a plan to maintain its readiness?

    c. Can the region implement the plan when a storm strikes?

    d. Does the plan employ standardized metrics that are defined by credible organizations?

    e. Does an independent entity review the assessment process on a periodic basis?

    Professional analysts will inevitably agree on certain answers to these questions and disagree on others. It’s important, though, that they all utilize the same publicly available information to develop their informed opinions. For instance, regarding the Hill Country case:

    a. The web site of the Texas Water Development Board’s State Flood Planning function contains material information about flood assistance programs, management training, community resources, and other relevant functions.

    b. The web site of the Development Board’s State Flood Plan contains significant supporting information about the state’s “… effort to perform comprehensive planning to reduce flood risk and take a broad look at flood hazard across the state.”

    c. The 245 page PDF document entitled 2024 State Flood Plan is the most recent comprehensive state-wide plan; thus, it reflects the current status of the region’s readiness to respond to catastrophic floods.

    Reasonable minds may certainly differ about how the data in such sources may impact an assessment of a regional response function. Nevertheless, reasonable minds should all agree on the relevance of the (above) five questions and the need to utilize a region’s published information for assessment purposes.

  • A.I. Queries, Implicit Variation, and the Practice of Due Professional Care

    Are you aware that your A.I. queries provide documentary evidence of your practice of “due professional care” in researching business information? If you utilize an electronic platform with A.I. technology to search for business data, you could be held legally responsible for relying on inappropriate information that is generated by flawed queries.

    Let’s review an example or two of a flawed query. On June 19, 2025, I submitted the following query to Google Gemini: Please create an image of a person who is conducting his banking business at a very well managed community bank with strong internal controls. Gemini replied: Here is an image of a person conducting banking business at a well-managed community bank: 

    Then I initiated a new chat and submitted the following query: Please create an image of a person who is conducting his banking business at a very poorly managed community bank with weak internal controls. Gemini replied: Here is the image you requested: 

    Can you see the implicit variation? It’s not an easy task. Indeed, at first glance, both images appear to present a community bank that is serving a customer’s needs. And neither is employing a clearly visible set of particularly strong (or weak) internal controls.

    But look more closely. In comparison to the well-managed bank, the poorly managed institution uses far more paper to process transactions. The customer is informed of business procedures via a cluttered array of temporary signs. Even the technology in the poorly managed bank is inferior, as evidenced by the obsolete “chunkiness” of the computer screens.

    Those factors do not, however, represent terribly weak internal controls in community banks. After all, it is certainly feasible to effectively manage a paper-based business with temporary signs and relatively old technology. And the common factors that normally differentiate strong control environments from weak control environments, such as a visible security presence and enclosures that protect the privacy of shared financial information, do not exist in either image.

    There’s also a subtle bias that is embedded in the images. The well managed bank features younger individuals with friendly facial expressions who wear more stylish clothes. The poorly managed bank features older individuals with neutral facial expressions who are less stylish in appearance.

    Those are not distinctions involving internal controls. They are marketing images that pervade the internet. And yet Gemini offers them as differential factors that illustrate relatively strong or weak management conditions.

    These are two visual examples of the types of queries that may produce undesirable implicit variation in A.I. output. Because they lack specific information, the queries can be “red flagged” as deficient by auditors as falling short of the minimum standards of “due professional care.”

    What specific information? Co-founder and current President of OpenAI (the firm that owns ChatGPT) Greg Brockman uses layperson’s language to define what Inc. called the basic structure of the perfect AI prompt. To exercise an appropriate degree of “due professional care,” all firms should integrate such guidance with other supplemental material to develop their own policies and procedures for defining A.I. queries.

    End Note: Many thanks to my colleague Alan White of CU Accelerator and the Association of Credit Union Audit and Risk Professionals for their invitation to present this information at the ACUARP’s 35th Annual Insight Summit in San Antonio TX next week.

  • The Perrier Mineral Water Violation: Sustainability Enters The Business Mainstream

    Is this really a “natural” process?

    Last month, the French government accused Nestle of a serious legal violation. Although the firm has always advertised its Perrier mineral water brand as one that represents “natural” water, the government charged that Nestle actually disinfects and processes the water before bottling it. Thus, according to French law, it isn’t “natural” at all, and must not be advertised in that manner.

    Before sustainability accounting and reporting became a public concern twenty to thirty years ago, this event would have been perceived as an internal management issue. In addition, it would have been seen as an external brand management and public relations matter.

    So what has the rise of sustainability accounting and reporting processes contributed to our ability to assess Nestle’s exposure to this type of problem? We can now review Nestle’s Performance and Reporting web site section, noting that the firm utilizes the two major sustainability reporting standards (the GRI and the SASB) to guide its selection of sustainability metrics.

    We can also see that Nestle employs a Big Four global public accounting firm (EY) to perform assurance activities on those metrics. And we can compare Nestle’s supply chain management and water processing measurement and reporting choices to the choices that were made by its major competitors, thereby assessing its levels of corporate performance against comparative industry data.

    Conversely, the absence of reported data may compel us to feel concern. Nestle does not appear to rely on the U.N Sustainable Development Goals (SDGs) to the same extent that it does the GRI and the SASB. The SDGs provide an important complementary view because they define sustainability metrics from a public interest perspective.

    Furthermore, Nestle does not directly address the impact of its “natural” emphasis on its brand value. That’s an important factor because Interbrand has estimated that the Nestle brand is the 71st most valuable brand in the world. However, Interbrand estimates that the value of its brand shrank by 1% last year; thus, controversies involving its Perrier product line may reinforce this negative trend.

    Clearly, a business analyst who is ignorant of this sustainability data will be unable to assess the implications of Perrier’s “natural” controversy in a comprehensive manner. Likewise, an analyst who focuses exclusively on sustainability data (and not at all on supply chain or brand valuation information) will not be able to assess the implications either.

    These observations explain why many corporations have not created fully staffed “Departments of Sustainability” in their organizations. It’s not that the firms are ignoring sustainability business processes. In fact, the reverse is true; these companies have indeed embraced such practices, and have absorbed them into their mainstream business activities.

  • The Implications Of “Peak Shale”

    Estimating The Depletion Rate Of The Natural Asset

    During the past fifteen years, technological advancements in extracting crude oil from shale rock have propelled the United States into the unfamiliar role of the world’s top oil producer. Recently, though, representatives at several leading fossil fuel companies predicted that American oil production is peaking and will soon decline.

    The causal factors that they cited for their “peak and decline” prediction are all foreseeable ones. There is a limited amount of crude oil reserves in American territory that can be extracted in an affordable manner, and much of it has already been processed by oil companies. Furthermore, the market sales price of crude oil is very low by historical standards, and thus there is limited revenue to pay for the costs of production. Finally, the costs of production are increasing for various inflationary reasons, further reducing potential profits.

    Business planners and data modelers throughout many industry sectors may need to factor “peak shale” into their forecasts and projections. As domestic crude is replaced by imported substitutes, the increased costs of raw materials will “flow through” into higher costs of finished goods. Other production materials that are transported by fossil fueled cargo ships, airplanes, and freight trucks will also generate cost increases. And government tariffs on imported products will further increase costs.

    Supply chains may also become more unstable. Domestic oil production is less vulnerable to many types of disruptions, such as weather events, political crises, and social protests. Product shortages may thus become more common throughout the American economy.

    None of these potential impacts is necessarily new to U.S. industry sectors, though. During the 1970s, for instance, the Arab oil embargo caused severe shortages and price inflation in the gasoline and other energy markets. For a brief period of time, the Securities and Exchange Commission (SEC) and the Financial Accounting Standards Board (FASB) both mandated special financial reporting requirements involving “changing prices.”

    Thus, planners and modelers do have historical examples that can be consulted for guidance. Although the American economy has not experienced these phenomena recently, it may be reassuring to keep in mind that they are not black swan events.

  • End-To-End Supply Chain Management Systems: Have A Cup Of Coffee!

    Do you know where your favorite beans were grown?

    How has life changed for organizations in this era of tariff-induced supply shortages? For starters, it’s more important than ever to prioritize the risk that running out of a single critical part can halt an entire production process.

    As a result, holistic and comprehensive end-to-end supply chain management systems are soaring in popularity. Enterprise Resource Planning (ERP) systems may have existed for three decades, but they are now considered more important than ever.

    Thus, organizations that seek to gain a market advantage over their direct market competitors may choose to look outside of their industry sectors for ERP exemplars in redesigning end-to-end supply chains. If you’re engaging in such an activity, you may decide to have a cup of coffee.

    Why? Because coffee bean growers, distributors, and sellers are now adopting the most advanced supply chain management systems to comply with the European Union’s Deforestation Regulation (EUDR). This regulation requires that companies throughout the global coffee supply chain institute the internal controls, capture the data, and then complete the necessary “due diligence” to provide assurance that the industry is operating in an environmentally appropriate manner.

    The EUDR has been delayed, but it’s still on schedule for implementation at the end of this year for large firms and the end of next year for smaller ones. It can take a very long time, though, to develop the necessary systems to comply with it.

    Thus, developmental guidance already exists for organizations that are required to implement it. If you’re searching for such guidance to inform your own efforts, you may wish to grab a cup of coffee and review the documentation.

  • Entrepreneurial Capital: A Very Old Sector Produces New Options

    Will he “take his company public” on NYSE, NASDAQ, TXSE, or GIX?

    Are you an entrepreneur in need of capital? You may have been focusing on private equity, venture capital, and other alternative investment sources. Next year, however, an unexpected sector may present you with new opportunities.

    It’s the Stock Exchange sector, a source of capital that has existed for centuries. Operating under the TXSE acronym, the new Texas Stock Exchange will launch in Dallas. Meanwhile, using the GIX moniker, a group of former New York Stock Exchange executives will launch a new sustainability-themed entity called the Green Impact Exchange.

    During the nineteenth and twentieth centuries, numerous Stock Exchanges operated across the United States. Most of them, however, merged into a handful of trading conglomerates. The NASDAQ, for instance, acquired the Philadelphia Stock Exchange after the venerable organization absorbed the Baltimore, Washington, and Pittsburgh Exchanges.

    Until recently, few believed that newly formed Stock Exchanges could compete with much larger rivals in a cost-effective manner. New technologies, though, are expected to level the playing field and bring significant cost efficiencies to these smaller entities.

    Furthermore, the ongoing evolution of the American economy may enable certain entrepreneurs to leverage multiple new opportunities. Because Texas is now the nation’s largest producer of renewable energy, for instance, regional entrepreneurs in the sustainability field may be eligible to consider both the TXSE and the GIX for their capital requirements.

    Will these Exchanges find a niche in the investment sector? They’ll clearly face many competitors in the market. Nevertheless, the new entities will likely improve conditions for entrepreneurial firms in need of capital.

  • AI On The Road: Driverless Freight Trucks To Begin Cruising Interstate Highway This Month

    A driverless freight truck delivers its cargo.

    Since Artificial Intelligence (AI) systems seized the spotlight with the initial release of ChatGPT in November 2022, business analysts have been waiting for the technology to be placed in widespread use in major economic sectors. The outcome metrics from such case applications are required to assess the financial implications of the technology on sales, cost, distribution, insurance, and other functions of their business models.

    One such case application was just announced by Axios and other news platforms. Aurora Innovation, a self-driving vehicle technology firm, has been testing its driverless (but with human passengers available to assume control) freight trucks in northern Texas. By the end of this month, it plans to begin directing driverless trucks on Interstate 45 between Dallas and Houston.

    Driverless freight trucks barreling down an interstate highway? What could go wrong? Will the benefits exceed the risks? Business analysts expect to find out soon.

    If you wish to track Aurora’s progress, you may visit:

    Freight trucks, of course, play a centrally important role in the supply chains of many industry sectors. Driverless freight is thus an attractive sector to begin assessing the business impacts of AI technology.

  • United Airlines’ Scenario-Based Investor Guidance Isn’t Worrisome At All

    Two scenarios produce two sets of investor guidance.

    United Airlines is drawing attention over its recent decision to release two different sets of investor guidance. Some commentators are correctly noting that two sets of guidance is a “novel” occurrence.

    Usually, companies release one set of guidance that reflects its assumptions about the economy’s probable future path. Alternatively, on rare occasions, companies may decline to release any guidance at all, asserting that the economy (at that moment) is so unpredictable that any information would be too speculative to be useful.

    So why two? And why now? According to United’s economic forecasters, we will either experience an economic recession in the near future, or we will not. If we do, the future will feature one set of economic conditions. If we don’t, it will feature a very different set.

    That’s a classic case of circumstances that warrants an approach called Scenario Analysis. It’s different than Sensitivity Analysis, an approach that is employed when future conditions are relatively predictable, but when specific quantitative assumptions (such as sales volume or the cost of labor) may vary by a few percentage points in either direction. 

    In Scenario Analysis, one can define two or more (very different) potential future paths. One can also define the economic conditions that would exist for each potential path. However, one cannot be certain (and, instead, one is very uncertain) which specific future path is more likely to become our future reality. 

    That’s the situation in which United finds itself today. It may be novel from a historical perspective, but it isn’t worrisome at all, given the extremely volatile state of our contemporary global economy. Thus, it would not be surprising if other companies adopt United’s approach and decide to issue multiple sets of guidance for their own investors.

  • A Plastics Power Hour

    Several accounting standards address the environmental risk of plastics and microplastics.

    Many thanks to Johns Hopkins School of Advanced International Studies (SAIS) graduate student Shiyuan Liao for an objective and sobering webinar presentation about the uses and risks of plastics in our supply chains and product lines. The webinar was sponsored by SolPods, a nonprofit student-focused community in the sustainability field, and introduced by its Co-Founder and Executive Director Amy Farrell.

    Here in the energy corridor of Houston TX, plastics is a major industry sector. Petroleum (i.e. crude oil) is transformed into petrochemicals, which in turn is utilized to manufacture plastics. Thus, plastics is a significant wealth driver in our regional economy.

    The recent news of microplastics being detected in human brains reminds some of the historic case of the now-banned insecticide DDT entering human bodies through the food chain and then causing damage in successive generations that inherit the chemical as fetuses.

    Fortunately, unlike the situation in the DDT case, medical researchers have not definitively identified any harmful impacts of the presence of these microplastics in human bodies. Nevertheless, it is obviously a potential concern, one that was addressed by Shiyuan Liao in the Q&A discussion after her presentation.

    Interested in learning more about the topic? You may wish to reference the Sustainability Accounting Standards Board’s measurement and reporting standards for Chemicals. It can be accessed by visiting the “Resource Transformation” industry sector of the SASB standards.

    Plastics and microplastics are also covered by the Global Reporting Initiative’s standards for Agriculture, Aquaculture, and Fishing (GRI 13), for Materials (GRI 301), and for Waste (GRI 306). Although the first standard only applies to a single industry sector, the latter two apply to every sector.

    Finally, the Target Indicators of the United Nations’ Sustainable Development Goals covers plastic debris density in SDG 14.1 (Life Below Water, Marine Pollution).

    Incidentally, these resources are all freely viewable or downloadable on the following web pages:

    sasb.ifrs.org/find-your-industry

    globalreporting.org/standards/download-the-standards

    sdgs.un.org/goals

    As you can see, the topic of plastics is a deeply complex one. Again, many thanks to Shiyuan Liao and SolPods for educating us on the subject matter.

  • Staying Focused On Regulatory Risk

    Corporations may struggle to manage the risk of massive environmental losses.

    What is the greatest short term threat to financial profitability? For many organizations, the obvious answer is now “protective tariffs.” That’s a reasonable reply, but regulatory risk may represent a potentially greater long term threat to corporate performance.

    Earlier this week, for instance, Chevron lost an environmental lawsuit and was ordered to spend three quarters of a billion dollars to restore a damaged region of Louisiana wetlands. In certain respects, the case was reminiscent of General Electric’s historic half a billion dollar legal order to remove toxic PCB waste from the bed of the Hudson River.

    And a few days ago, the German financial institution Deutsche Bank was assessed a “greenwashing” penalty of 25 millions euros for making misleadingly rosy statements about its ESG practices. Though tiny in comparison to the Chevron penalty, the assessment served as a regulatory signal that firms will be held liable for unsupported public statements regarding sustainability and resilience.

    Both firms faced regulatory risks related to sustainability concerns. Only Chevron, though, confronted a concern involving actual environmental damage. Thus, it “paid the price” in the form of a far greater penalty.

    Tariffs can inflict tremendous harm on an economic system, but they can appear or disappear in the blink of an eye. Regulatory risks regarding sustainability and resilience, on the other hand, can impose damages that may linger indefinitely.

  • The Trump Tariff System And The Role Of Sustainability Accounting

    Earlier in American history, federal custom houses inspected imports and levied tariffs.

    A few hours ago, President Donald Trump issued his long-awaited announcement regarding a new tariff system for imports into the United States. The system includes an emphasis on “non-monetary barriers” that compel the U.S. federal government to assess higher tariff rates against certain countries.

    The three examples of “non-monetary” barriers that Trump emphasized were currency manipulation, unfair tax policy, and the use of sweatshop labor.

    Sweatshop labor is a very obvious example of a social sustainability factor. Unfair tax policy is defined as an economic sustainability factor by sustainability standards organizations like the GRI.  Only currency manipulation generally falls outside of most definitions of sustainability and resilience.

    Sustainability accountants will obviously be needed to measure the performance of importers to assess whether nations, or specific companies within nations, are meeting or avoiding these threshold definitions of “non-monetary barriers.”

    President Trump has never described himself as a proponent of sustainability accounting. Given the need for metrics that define the parameters of “non-monetary barriers,” though, he may agree that sustainability accounting will play an important role in the nation’s new tariff system.