AI Boom Watch: The Titans, The Tools, and The Threats

In this post, we look at several stories related to the AI boom and how giant tech companies are profiting handsomely from the current hype cycle. We'll also touch on major developments at Alphabet, Nvidia, Grammarly (now Superhuman), and OpenAI's potential IPO plans.

However, as a CPA, what really caught my attention was the first article about how AI is being used to create fraudulent receipts for travel expense reports. I've been wondering how AI challenges would make their way into our profession, and here we are.

This story highlights the new reality that you cannot believe your eyes anymore. Receipts submitted for expense reports may be AI-generated fakes that are extremely difficult to detect. Blake Oliver, CPA, and David Leary, hosts of The Accounting Podcast, demonstrate live how easy it is to create convincing fake receipts with ChatGPT – complete with crinkles and the coffee stains. (Check out AppZen's take on this.)   

So, what does this mean for us when evaluating audit evidence?

Tools like Decopy's AI Image Detector offer one potential solution by analyzing metadata. However, metadata analysis won't be effective if someone takes a screenshot of the AI-generated image and submits that instead. This poses a significant challenge since visual inspection of documents has traditionally been one of our primary verification methods.

Currently, this issue appears mostly at the employee expense level. I haven't yet seen evidence of this manifesting in actual audit evidence: though it would take quite the fraudster to use such techniques in financial statement fraud.

However, if you recall Barry Minkow from the ZZZZ BestCarpet Cleaning scandal of the 1980s, he did not have access to AI. Instead, he had access to the advanced technology of the age: the photocopier. Used this advanced tech Minkow faked the documentation required to pass the financial audit. What's the difference between then and now? The barrier to entry for such fraud has drastically lowered—you no longer need access to expensive advanced technology, just a subscription service for a few dollars a month.

Ultimately, it comes down to incentives. When people get desperate to prop up company valuations, as we saw with ZZZZ Best, fraud can occur. The question is: will difficult economic times ahead provide the incentives to encourage such fraud?

AI-Powered Expense Fraud Surges as Fake Receipts Fool Employers

AI-generated fake receipts are driving a new wave of expense fraud, with businesses now facing a sharp rise in undetectable falsified documents. AppZen reported that 14% of fraudulent expenses in September 2025 were AI-generated, up from 0% in 2024. These increasingly sophisticated documents are proving challenging even for expert reviewers to spot, prompting firms to consider metadata-based verification. With AI-driven deception becoming common in hiring, education, and finances, companies are grappling with new operational risks in an era where seeing is no longer believing. (Source: TechRadar)

• AI-generated receipts drive a new fraud wave: Businesses saw a spike in fake expense documents, rising to 14% of all fraudulent claims in just one year.
• Detection tools struggle to keep up: Even trained reviewers and software are struggling to detect sophisticated AI-generated receipts, increasing the burden on companies.
• Fraud reflects broader AI misuse: From hiring scams to academic cheating, AI-powered deception is becoming a systemic challenge across industries.

Tech Titan’s AI Bet Pays Off: Alphabet Posts $35B Profit in Q3

Alphabet reported a record-breaking $102.3 billion in Q3 revenue, boosted by surging demand in cloud computing and digital advertising, along with aggressive AI investments. Net income hit $35 billion, and the company raised its AI-related capital expenditure forecast to as high as $93 billion for 2025. CEO Sundar Pichai emphasized the tangible business impact of AI, particularly via the Gemini AI model now used in Google Search and YouTube. While Google faces regulatory pressure, recent court decisions have favored the company, allowing it to maintain vital partnerships like the one with Apple. (Source: WSJ)

• Record-breaking quarter for Alphabet: The company reported $102.3 billion in revenue and $35 billion in profit, driven by strong growth in cloud computing and digital advertising.
• AI investment ramps up: Google raised its capital expenditure forecast to as much as $93 billion for 2025, focusing heavily on AI infrastructure and product integration.
• Navigating regulatory pressure: While facing multiple antitrust challenges, recent legal decisions have largely favored Google, preserving key business arrangements like its deal with Apple.

Nvidia Becomes First $5 Trillion Company Amid AI Chip Surge

Nvidia made history by reaching a $5 trillion market valuation, propelled by its dominance in AI chips and soaring investor confidence in the AI boom. CEO Jensen Huang announced $500 billion in chip orders and plans for U.S. supercomputers, further solidifying Nvidia’s status at the center of AI infrastructure. Despite emerging competition and geopolitical friction over chip exports to China, the company’s H100 and Blackwell processors remain essential to powering major AI applications like ChatGPT. (Source: CBC)

• Historic valuation milestone: Nvidia became the first company to hit a $5 trillion valuation, fueled by explosive AI demand and strategic dominance in AI chipmaking.
• CEO Huang's growing influence: With $500B in chip orders and new U.S. supercomputers planned, Huang's leadership is reshaping the AI landscape and increasing U.S. investment.
• Global power dynamics at play: Nvidia is at the center of U.S.-China tech tensions, balancing geopolitical pressures while maintaining its leadership in cutting-edge AI hardware.

Grammarly Rebrands as Superhuman to Launch Unified AI Productivity Suite

Grammarly has rebranded to Superhuman, expanding beyond grammar checks to offer a comprehensive AI productivity suite. This includes Grammarly’s original tool, the Mail email service, Coda collaborative workspace, and Superhuman Go—AI agents designed to streamline professional workflows. The pivot follows acquisitions of Coda and Superhuman, and the company is now bundling these tools under one subscription. With a user base of 40 million and $700 million in revenue, Superhuman is targeting measurable productivity outcomes, especially for enterprise clients. (Source: BetaKit)

• Grammarly evolves into Superhuman: The rebrand marks a shift to an AI-driven productivity suite combining writing, email, collaboration, and AI agents.
• Strategic acquisitions power growth: Recent purchases of Coda and Superhuman enable the company to unify tools into a seamless, context-aware platform.
• Enterprise focus with measurable results: Superhuman aims to prove ROI to clients, highlighting a 16% improvement in customer satisfaction in pilot tests.

OpenAI Eyes $1 Trillion IPO as It Preps for Historic Public Debut

OpenAI is exploring a public listing that could value the company at up to $1 trillion, with potential IPO filings starting in late 2026. The move follows a major restructuring that reduced its reliance on Microsoft and gave its nonprofit foundation a significant financial stake. OpenAI expects to reach a $20 billion revenue run rate by year-end and aims to raise massive capital for upcoming AI infrastructure projects. CEO Sam Altman acknowledged that going public is the most likely path given the company’s future financial needs. (Source: Reuters)

• IPO could hit $1 trillion valuation: OpenAI is preparing for a public offering as soon as late 2026, aiming for a valuation that would place it among the most valuable companies ever listed.
• Restructuring unlocks financial agility: A recent overhaul separates governance from operations, enabling capital raises and acquisitions while preserving nonprofit oversight.
• Massive capital needs ahead: CEO Sam Altman plans to pour trillions into AI infrastructure, making public markets a critical funding source for OpenAI’s ambitious roadmap.
  

Author: Malik D. CPA, CA, CISA. The opinions expressed here do not necessarily represent UWCISA, UW,  or anyone else. This post was written with the assistance of an AI language model. 

The AI Bubble in Focus: Why It Feels Familiar

This week, we’re diving into something that’s hard to ignore right now: the AI bubble. 

The idea came from a Bloomberg graphic showing the circular flow of money within the AI ecosystem. Although I saw it before, I saw it again on YouTube. So I thought it would be a good idea to focus this week's post on the topic. 

From: Here

Bubbles are nothing new. They’re part of capitalism’s DNA. A good framework to think about this is the Gartner Hype Cycle. It maps out two main forces that shape how technology evolves. The first is the S-curve — that natural, steady climb of genuine technological progress. The second is the hype curve — that euphoric rush of money and optimism that tends to overshoot what the tech can actually do.

That gap between expectation and reality is where the trouble usually starts. It’s also where Gartner’s so-called trough of disillusionment begins — and, as Gartner points out, generative AI has officially entered that stage. If you’re not familiar with the hype cycle, it’s worth checking out. It helps make sense of why so many people are starting to feel that uneasy mix of excitement and skepticism right now.

This topic also connects back to some early research I did with Professor Efrim Boritz at the University of Waterloo on the concept of bubbles: work that actually came out just before Gartner released their model. We looked at how bubbles have shown up again and again: the railway bubble, the radio bubble that set the stage for the 1929 crash, the dot-com bubble, and so on. These aren’t random events; they’re patterns.

So yes, it’s probably fair to say we’re in a bubble now. That’s not investment advice (I am in risk management after all!): just an observation based on history. The Bloomberg piece and its “circular flow” chart tell one side of the story, but the other side is economic: the Magnificent Seven tech giants are booming while the rest of the economy struggles. That imbalance matters, and it could have some dramatic ripple effects.

And, if history is any guide, when the music stops, auditors and accountants are usually among the first to face the spotlight — whether they deserve it or not. New accounting rules and oversight frameworks always seem to appear after something breaks. Think about it:

• The Savings and Loan crisis in the ’80s gave us the COSO framework and the Treadway Commission.
• The Enron and WorldCom scandals led to Sarbanes–Oxley.
• The 2008 financial crisis brought Dodd–Frank.

So the real question isn’t just whether there’s an AI bubble — it’s what will come after it bursts. Every bubble leaves behind more than just wreckage; it reshapes how we account for risk, trust, and innovation.

AI at the Crossroads: Boom, Bubble, or Rebuild?

1. Inside the $1 Trillion AI Boom: OpenAI’s Circular Deals with Nvidia and AMD

OpenAI has struck massive, multi‑billion dollar deals with both Nvidia and AMD in an effort to secure the computing power it needs to stay ahead in the AI race. These agreements—up to $100 billion with Nvidia and a significant multi‑gigawatt arrangement with AMD—are fueling what experts predict could be a $1 trillion AI infrastructure surge. But the structure of these deals, with equity swaps and reciprocal commitments, has raised concerns that the growth may be more circular than sustainable. Analysts warn that while this could reshape the AI hardware ecosystem, it also introduces new risks around transparency, regulation, and long‑term value. (Source: Bloomberg)

• OpenAI’s infrastructure expansion: The company is scaling its compute capabilities dramatically, including a 10 gigawatt GPU commitment from Nvidia.
• Circular investment structures: Deals involving equity stakes and purchase commitments between OpenAI, Nvidia, and AMD raise questions about the sustainability and true demand of AI infrastructure growth.
• Market risks and scrutiny: Despite the potential for a $1 trillion AI boom, experts highlight concerns about profitability, supply chain limitations, and looming regulatory oversight.

See here for Bloomberg Intelligence's coverage of this.

2. Investors Revisit 1999: How the AI Boom is Echoing the Dot‑Com Era

As AI investment fever grips global markets, many investors are turning to old strategies to navigate what could be another tech bubble. Reuters reports that hedge funds and asset managers are pulling back from the most overhyped AI stocks and shifting toward undervalued adjacent sectors like robotics, clean energy, and Asian tech. The article draws sharp comparisons to the dot‑com boom, pointing out the concentration of market performance in a few companies and the increasingly speculative nature of some AI plays. (Source: Reuters)

• Investor strategy adjustment: Instead of piling into top AI‑stocks, many are repositioning into overlooked sectors (e.g., robotics, Asian tech, uranium) to ride the wave while avoiding peak‑risk.
• Echoes of dot‑com excess: The environment mirrors 1999‑2000’s tech boom—with extreme valuations, concentration in a few companies, and risks of overcapacity and hype.
• Dual scenario risk: If AI delivers as promised, investors will be rewarded; but if the productivity gains don’t materialize or costs escalate, a sharp correction could follow.

3. Hype Cycle Refresh: What does Gartner say about AI & Hype? 

According to Gartner’s 2025 Hype Cycle for Artificial Intelligence, GenAI has officially entered the “Trough of Disillusionment” as organizations begin to grasp its limitations. While many struggle to prove ROI on AI investments, the attention is shifting toward foundational technologies like AI-ready data, AI agents, and ModelOps. These building blocks are seen as critical for operationalizing AI at scale and ensuring long-term success. The report also notes a growing emphasis on governance, security, and real-world deployment, marking a maturation of enterprise AI strategy. (Source: Gartner)

• GenAI’s changing role: Generative AI has reached the “Trough of Disillusionment” as expectations meet reality and many organizations fail to see clear returns.
• Foundational technologies rising: AI‑ready data and AI agents are among the fastest‑moving innovations in 2025, showing where investment is shifting for scalable AI.
• Governance and operations matter: For AI to deliver value, enterprises must focus on infrastructure (ModelOps), governance (risk, bias, security), and data management — not just on building large models.

4. When AI Powers the Market: How the Infrastructure Boom Is Shaping Stocks

The U.S. stock market’s recent highs are largely fueled by AI-related stocks. Investopedia details how massive capex from tech giants like Microsoft, Alphabet, and Meta is driving growth in chipmakers and software companies, many of which are seeing their stock prices soar. But the article also warns that “circular” investments—where companies fund each other while purchasing each other’s products—could be fragile. If investor sentiment shifts or AI returns disappoint, the entire market could face a downturn. (Source: Investopedia)

• AI stocks as market engines: Many of the top‑performing stocks in the S&P 500 are tied to AI and have helped sustain the broader bull market.
• Massive infrastructure build‑out: Tech giants are significantly increasing capex to support AI infrastructure, which is fueling growth in chipmakers and related firms.
• Bubble risks loom: The article warns that circular deals and high valuations could leave the market vulnerable if AI investment returns don’t meet expectations.

5. The AI Bubble: What will be the Bloody Aftermath?

Eduardo Porter’s piece in The Guardian takes a sobering look at the economic fragility masked by AI’s explosive growth. While tech investment is propping up stock prices and business activity, the real economy—wages, employment, consumer stability—is showing signs of stress. Porter argues that a collapse of the AI bubble might be painful but necessary, providing an opportunity to reorient AI development toward augmenting rather than replacing human labor and to address the concentration of wealth and power in tech giants. (Source: The Guardian)

• Economic fragility behind the boom: Despite dazzling investment in AI, fundamentals like employment growth and wages are weak — signalling underlying fragility.
• The bubble risk with wide consequences: If the AI‑investment bubble bursts, the fallout wouldn’t just hit tech companies — the broader economy could follow.
• A potential reset with social opportunity: The article suggests that a correction could open the door to re‑orienting AI toward human‑centric outcomes and more equitable economic structures.

Author: Malik D. CPA, CA, CISA. The opinions expressed here do not necessarily represent UWCISA, UW,  or anyone else. This post was written with the assistance of an AI language model.