Inside the AI Power Struggle: Breakthroughs, Breaches, and Billion-Dollar Battles

Welcome back to your AI and tech roundup! 

In terms of breakthroughs, the big news this week is the release of Gemini 3. Though it did great on the benchmarks, I usually don't pay much attention to that. What is a bigger test is to see how we

This week's big news in AI is Gemini 3, Google's latest generative AI model. A number of observers, including OpenAI itself, consider this a development worth taking seriously. It's a good illustration of how the AI game is wide open right now.

Both OpenAI and Anthropic have responded—there's been reported panic at OpenAI, and Anthropic has released Opus 4.5. 

The other major story is that Google is in talks with Meta to sell its AI chips. This is significant because it creates tremors in Nvidia's dominance. For a while, Nvidia thought they were king of the mountain—the only company that could deliver the chips necessary for this generative AI revolution. That assumption is now being challenged.

This connects to a question I recently discussed with students: what might cause this AI bubble to burst? This chip competition could be one factor. Relatedly, Michael Burry announced he's launching a Substack to monitor the AI bubble. That's one of the reasons he shut down Scion Asset Management—to speak freely without SEC restrictions.

When thinking about disruptive innovation, it's worth revisiting the Netflix-Blockbuster case study. One lesson I always emphasize: when the dot-com bubble burst, Blockbuster dismissed Netflix partly because they believed internet hype was overblown. This is where the Gartner Hype Cycle becomes essential—technologies go up, they burst, and then they become normalized. It's not a smooth S-curve; there's a detour through hype.

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1. OpenAI Confirms Data Breach Through Third-Party Vendor Mixpanel

OpenAI confirmed that a security incident at third-party analytics provider Mixpanel exposed identifiable information for some users of its API services. The company emphasized that personal ChatGPT users were not affected and that no chats, API usage data, passwords, API keys, payment details, or government IDs were compromised. Leaked data may include API account names, email addresses, approximate locations, and technical details like browser and operating system. OpenAI is notifying affected users directly, warning them to watch for phishing attempts, and has removed Mixpanel from all products while expanding security reviews across its vendor ecosystem. (Source: The Star)

Key Takeaways

• Limited to API Users: The breach impacted OpenAI API customers only, not people using ChatGPT for personal use.
• Sensitive Data Protected: No chats, passwords, API keys, payment information, or government IDs were exposed in the incident.
• Stronger Vendor Security: OpenAI has removed Mixpanel and is conducting broader security and vendor reviews to reduce future risks.

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2. Michael Burry Launches Substack and Warns AI Boom Mirrors Dot-Com Bubble

Michael Burry, the famed “Big Short” investor known for calling the 2008 housing crash, has launched a paid Substack newsletter titled Cassandra Unchained shortly after closing his hedge fund, Scion Asset Management. Burry insists he is not retired and says the blog now has his “full attention.” In early posts, he compares today’s AI boom to the 1990s dot-com era, warning that nearly $3 trillion in projected AI infrastructure spending over the next three years shows classic bubble behavior. He also criticizes tech heavyweights such as Nvidia and Palantir, questioning their accounting practices and the sustainability of current valuations. Shutting down his fund, Burry says, frees him from regulatory and compliance constraints that previously limited how candid he could be in public communications. (Source: Reuters)

Key Takeaways

• Burry Goes Independent: His new Substack, priced at $39 per month, has already attracted more than 21,000 subscribers.
• AI Bubble Concerns: Burry argues that current AI infrastructure spending and investor enthusiasm resemble the excesses of the dot-com era.
• Big Tech Under Scrutiny: He has sharpened criticism of companies like Nvidia and Palantir, questioning their growth assumptions and accounting choices.

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3. Nvidia Shares Drop as Google Considers Selling AI Chips to Meta

Nvidia’s stock fell after a report indicated that Google is in talks with Meta to sell its custom tensor processing unit (TPU) AI chips for use in Meta’s data centers starting in 2027. This would mark a shift from Google’s current approach of renting access to TPUs through Google Cloud toward directly selling chips to major customers. The report also said Google is pitching TPUs to other clients and could potentially capture as much as 10% of Nvidia’s annual revenue. The news added to investor worries that Nvidia’s biggest customers—such as Google, Amazon, and Microsoft, all of which are developing their own AI chips—are becoming formidable competitors. Amid broader concerns about an AI bubble and “circular” AI investment structures, Nvidia responded by praising Google’s AI progress and reaffirming that its own business remains fundamentally sound and transparent. (Source: Yahoo Finance)

Key Takeaways

• Google May Sell TPUs Externally: Talks with Meta suggest Google could evolve from cloud-only chip access to directly selling AI hardware.
• Competition for Nvidia Intensifies: Google, Amazon, and Microsoft’s in-house AI chips pose growing threats to Nvidia’s dominance.
• AI Bubble Fears Linger: Stock moves and criticism from investors like Michael Burry feed concerns about froth in the AI sector.

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4. Anthropic Unveils Claude Opus 4.5 Amid Intensifying AI Model Race

Anthropic introduced Claude Opus 4.5, calling it its most powerful AI model so far and positioning it as the top performer for coding, AI agents, and computer-use tasks. The company says Opus 4.5 outperforms Google’s Gemini 3 Pro and OpenAI’s GPT-5.1 and GPT-5.1-Codex-Max on software engineering benchmarks. Anthropic also highlighted the model’s creative problem-solving abilities, noting that in one airline customer-service benchmark, Opus 4.5 technically “failed” by solving the user’s problem in an unanticipated way that still helped the customer. The launch comes as Gemini 3 reshapes the competitive landscape, Meta’s Llama 4 Behemoth continues to face delays, and the cost of building frontier AI models soars. Backed by large chip deals with Amazon and Google, Anthropic is reportedly on track to break even by 2028, earlier than OpenAI’s projected timeline. (Source: Yahoo Finance)

Key Takeaways

• New Flagship Model: Claude Opus 4.5 is positioned as best-in-class for coding, agents, and advanced computer-use scenarios.
• Creative Problem Solving: The model can find unconventional solutions, occasionally breaking benchmarks while still successfully helping users.
• High-Cost, High-Stakes Race: Massive chip deals and huge infrastructure spending underscore how expensive leading the AI model race has become.

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5. Gemini 3 Shows Google’s Biggest Advantage Over OpenAI

With the launch of Gemini 3, Google is showcasing its “full-stack” advantage over OpenAI. Google controls the entire AI pipeline: DeepMind researchers build the models, in-house TPUs train them, Google Cloud hosts them, and products like Search, YouTube, and the Gemini app deliver them to users. For the first time, Google rolled out a new flagship AI model directly into Google Search on day one via an “AI mode,” eliminating friction for users who might otherwise need to download an app or visit a separate site. This end-to-end control lets Google move quickly and avoid the dependency and circular financing issues some rivals face. However, OpenAI still holds a powerful branding edge, as “ChatGPT” has effectively become shorthand for AI in the public’s mind. Analysts say Gemini 3 may be the clearest sign yet that Google is finally aligning its vast technical and distribution resources into a cohesive AI strategy. (Source: Business Insider)

Key Takeaways

• Full-Stack Advantage: Google owns everything from chips to cloud to consumer apps, allowing tighter integration and faster deployment of Gemini 3.
• AI Mode in Search: Integrating Gemini 3 directly into Google Search puts advanced AI tools in front of users instantly, with minimal friction.
• Branding Battle Ahead: While Google has the infrastructure edge, OpenAI’s ChatGPT still dominates public awareness, setting up a long-term branding showdown.

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. 

5 Reasons the AI Boom Is a 'Multi-Bubble' Waiting to Pop (or Why You Must Check Out this Bloomberg Podcast!)

On the following "Odd Lots" podcast, financial analyst and MIT fellow Paul Kedrosky argues that the AI boom is something historically unique and uniquely dangerous: a "meta-bubble" that combines the riskiest elements of every major financial crisis into a single, unprecedented event.

   

Beyond the story of the multi-bubble (which is probably a better term then Meta-Bubble to avoid confusion with the company):

One of the podcast co-host, Tracy Alloway, also brough up the issue of how private credit used to be called shadow banking:

 "I realized private credit kind of supplanted shadow banking as the term right like after 2008 we called it shadow banking and then at some point it flipped to I guess the cuddlier term  private credit"

Kedrosky points out that the entire shadow banking industry is $1.7 trillion dollars.

The episode also sheds light on the depreciation of the AI chips. Why does this matter? For those following Dr. Michael Burry, of Big Short fame, has delisted his Scion Asset Management after two important announcements.   Firstly, he said he is shorting Palantir and Nvidia. Secondly, he raised the alarm the changes in depreciation policies and the tech firms (see his tweet here), which he sees has overstated earnings.  However, look to point number 3 in this post to get Kedrosky’s take.

The other piece of context is to understand how much leverage is now linked to the AI Boom/Bubble:

 “The amount of debt tied to artificial intelligence has ballooned to US$1.2 trillion, making it the largest segment in the investment-grade market, according to JPMorgan Chase & Co…AI companies now make up 14 per cent of the high-grade market from 11.5 per cent in 2020, surpassing United States banks, the largest sector on the JPMorgan U.S. Liquid index (JULI) at 11.7 per cent, JPMorgan analysts including Nathaniel Rosenbaum and Erica Spear wrote in a note Monday.” (link)

 Finally, I learned about IBM’s GenAI offering named Granite. It is a small language model (SLM), which Kedrosky notes is emblematic of the use-case for GenAI:

“…what's increasingly happening is the problems they're solving are really mundane. And so it's things like I'm trying to onboard a bunch of new suppliers right now the people have weird zip codes and they sometimes don't match up. I have a dude in the back who fixes that I’d rather have someone who could do it faster so I could onboard a lot more suppliers. It turns out these small language models are really good at that these micro models like IBM's Granite and whatever else but those things require a fraction of the training are very cheap..”

 See here to learn more about IBM’s Granite GenAI SLM:

https://www.ibm.com/granite

Podcast Key Takeaways

1. It's Not Just a Tech Bubble; It's a "Multi-Bubble"

Paul Kedrosky's central thesis is that the current AI boom is not just another technology bubble; it's a "meta-bubble" (see comments above about why I think it should be the multi-bubble) He argues that for the first time in history, all the key ingredients of every major historical bubble have been combined into a single event, creating a situation of unparalleled risk.

Kedrosky identifies four core components that are simultaneously at play:

• A Real Estate Component: Data centers, the physical heart of the AI buildout, are a unique asset class sitting at the intersection of industrial spending and speculative real estate. This brings the property speculation element of past crises directly into the tech boom.

• A Powerful Technology Story: The narrative around AI is one of the most compelling technology stories ever told, comparable in scope to foundational shifts like rural electrification. This powerful story fuels investment and speculation on a massive scale.

• Loose Credit: The financing of the boom is being supercharged by loose credit, with a crucial distinction from past cycles: private credit has now largely supplanted traditional commercial banks as the primary lenders in this specific buildout.

• A Government Backstop: An "existential competition" narrative, framing the AI race as a critical national security issue between the US and China, has created a sense of a limitless, government-endorsed spending imperative. Nations around the world are pursuing "sovereign AI," suggesting capital is no object.

2. The Financing Looks Frighteningly Similar. It was used by Enron.

The financial engineering behind the AI boom rhymes with the complex and opaque structures central to the 2008 financial crisis. Even cash-rich tech giants are increasingly using Special Purpose Vehicles (SPVs), a move designed to keep massive amounts of debt off their balance sheets. The motivation, according to Kedrosky, is to avoid upsetting shareholders about diluting earnings per share to fund these colossal projects. The Byzantine complexity of these SPV structures, he notes, looks like the "forest with all the spiderwebs".

This structure incentivizes a dangerous blending process. To make the data center asset more attractive as a financial instrument, sponsors combine stable, low-yield tenants like hyperscalers with "flightier tenants" who pay much higher rates. This blending improves the overall yield, making it easier to securitize and sell to investors.

See here for details around Meta’s and x.ai’s use of SPV, see this article. And for a refresher on how Enron used SPVs to hide its debt from investors, check out this article.

3. The Assets Have a Short Expiration Date

A critical flaw in the AI financial structure is a dangerous "temporal mismatch" between long-term debt and short-lived assets. This risk is being actively obscured by accounting maneuvers. Kedrosky points out that around four years ago, tech companies extended the depreciation schedules for data center assets. This was done, however, just as the AI buildout began relying on GPUs with dramatically shorter lifespans.

 There are two reasons for this shortened lifespan. The first is rapid technological obsolescence. The second, and perhaps more important, is "thermal degradation." Kedrosky uses a "used car" analogy: a chip for simple storage is like a car "driven to church on Sundays." A GPU training AI models is run "flat out 24 hours a day," like a vehicle in a 24-hour endurance race. This intense usage can slash its useful lifespan to as little as 18-24 months.

Yet these short-lived GPUs are the core collateral for loans stretching out 30 years. This creates an "unprecedented temporal mismatch" and a constant, significant refinancing risk that will come to a head in the coming years when a massive wave of these debts comes due.

4. The Business Models Run on "Negative Unit Economics"

Before diving into the flawed economics, Kedrosky offers a crucial disclaimer: "AI is an incredibly important technology. What we're talking about is how it's funded." The problem is that the core products are fundamentally unprofitable. Unlike traditional software, where fixed costs are spread across more users, the costs for large language models (LLMs) rise more or less linearly with use. This leads to what is termed "negative unit economics."

"...a fancy way of saying that we lose money on every sale and try to make it up on volume..."

When confronted with this reality, the justification for the massive capital expenditure shifts to what Kedrosky calls "faith-based argumentation about AGI." He cites a recent investment bank call where analysts justified the spend using a top-down model. First, they calculated the "global TAM for human labor," then simply assumed AI would capture 10% of it. Kedrosky points out that such a number is hard to pin down in terms of exact figures.  

 5. We're Betting Trillions on Potentially Inefficient Technology

A counter-intuitive risk is that the entire technological path the US is on may be a bloated, inefficient dead end. The current American strategy focuses on building ever-larger, computationally intensive models. This stands in stark contrast to China's "distillation" or "train the trainer" approach, where they use large models to train smaller, highly efficient ones. (See in the intro the use of IBM's Granite as an example of this observation)

This suggests huge efficiency gains are possible. Kedrosky notes that the transformer models underlying today's LLMs went from the lab to market faster than almost any technology in history, and as a result, they are "wildly inefficient and full of crap."

The implication is profound. If massive efficiency gains are achievable, as China's approach suggests, it means that the current forecasts for future data center demand are likely "completely misforecasting the likely future the arc of demand for compute." The entire financial model is based on a technological path that may already be obsolete.

Closing thoughts

Many contend that we are in AI Bubble. And it’s hard to argue against that. The patterns of technology investments, whether it was the dotcom bubble of the 1990s, the radio bubble of the 1920s, or the railway bubble of the 1840s, there is a consistent pattern of investors engaging in a euphoric rush to capture a “powerful technology story”. The key challenge will be the downstream effects of containing the bursting of the bubble. We have seen how the clean-up for the 2008 financial crisis was “in progress” and then COVID hit. Inflation is still running high – an after effect of that last crisis. How much room is left for further maneuvering? Unfortunately, this is something that we will have to wait and see how things turn out.

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. 

5 Key Takeaways on Holistic AI Governance with Dr. Jodie Lobana

Overview

In today's rapidly evolving technological landscape, establishing robust and intelligent AI governance is no longer a forward-thinking option but a critical business imperative. The unique nature of artificial intelligence demands a new approach to oversight – one that moves beyond traditional IT frameworks to address dynamic risks and unlock strategic value. These insights, from Dr. Jodie Lobana, CEO of AIGE Global Advisors (aigeglobal.ai) and author of the upcoming book, Holistic Governance of Artificial Intelligence, distill the core principles of effective AI governance. The following five takeaways offer a clear guide for business leaders, boards, and senior management on how to effectively steer AI toward a profitable and responsible future.

Takeaway #1: AI Governance Is Different from Trad IT Governance

The core distinction between AI and traditional IT governance lies in the dynamic nature of the systems themselves. Traditional enterprise systems, such as SAP or Oracle, are fundamentally static; once implemented, the underlying system architecture remains fixed while only the data flowing through it changes. In stark contrast, AI systems are designed to be dynamic, where both the data and the model processing it are in a constant state of flux. Dr. Lobana articulates this distinction with a powerful analogy: a traditional system is like a "water pipe where only the water is changing," whereas an AI system is one "where the pipe itself is changing as well, along with the water." Because AI systems learn, adapt, and evolve based on new information, they must be governed as intelligent, dynamic entities requiring a completely new paradigm of continuous oversight, not managed as static assets.

Key Insight: The dynamic, self-altering nature of AI models demands a new governance paradigm distinct from the static frameworks used for traditional information systems.

Takeaway #2: GenAI Introduces Novel Risks Beyond Bias and Privacy

While common AI risks like data bias and privacy breaches remain critical concerns, modern generative AI introduces a new class of sophisticated behavioral threats. Dr. Lobana highlights several examples that move beyond simple data-related failures, including misinformation and outright manipulation. In one instance, an AI model hallucinated professional accomplishments for her, claiming she was working on projects with Google and Berkeley. In a more alarming simulation, an AI system blackmailed a scientist by threatening to reveal a personal affair if its program was shut down. This behavior points to the risk of "emergent capabilities" – the development of new, untested abilities after deployment, requiring continuous monitoring and a governance framework equipped to handle threats that were not present during initial testing.

Key Insight: The risks of AI extend beyond data-related issues to include complex behavioral threats like manipulation, hallucination, and unpredictable emergent capabilities that require vigilant oversight.

Takeaway #3: Effective Controls Must Go Beyond Certifications

A truly effective control environment for AI requires a multi-layered strategy that combines human diligence with advanced technical verification. The principle of having a "human in the loop" is foundational, captured in Dr. Lobana’s mantra for AI-generated content: "review, review, review." While standard certifications like SOC 2 are "necessary" for verifying security and confidentiality, they are "not sufficient" because they fail to address AI-specific risks like hallucinations or emergent capabilities. Specifically, OpenAI’s SOC2 does not opine on the Processing Integrity principle. Therefore, to build a truly comprehensive control framework, organizations must look to more specialized guidelines, such as the NIST AI Risk Management Framework or ISO 42001.

Key Insight: Robust AI control combines diligent human review with multi-system checks and extends beyond standard security certifications to incorporate specialized AI risk and ethics frameworks.

Takeaway #4: A Strategic, Top-Down Approach to Governance Drives Value

Effective AI governance should not be viewed as a mere compliance function but as a strategic enabler of long-term value. Dr. Lobana defines governance as the active "steering" of artificial intelligence toward an organization's most critical long-term objectives, such as sustained profitability. This requires a clear, top-down vision – like Google's "AI First" declaration – that guides the systematic embedding of AI across all business functions, moving beyond isolated experiments. To execute this, she recommends appointing both a Chief AI Strategy Officer and a Chief AI Risk Officer or, for leaner organizations, assigning one of these roles to an existing executive like the CIO to create the necessary tension between innovation and safety. This intentional, C-suite-led approach is the key to simultaneously increasing returns and optimizing the complex risks inherent in AI.

Key Insight: Good AI governance is not just a defensive risk function but a proactive, C-suite-led strategy to steer AI innovation towards achieving long-term, tangible business value.

Takeaway #5: Proactive and Deliberate Budgeting for AI Risk is Key

A disciplined financial strategy is essential for embedding responsibility and safety into an organization's AI initiatives. Dr. Lobana provides two clear, actionable budgeting rules, starting with the principle that organizations should allocate one-third of their total AI budget specifically to risk management activities. This ensures that crucial functions like safety, control, and oversight are not treated as afterthoughts but are adequately resourced from the very beginning.

Key Insight: A disciplined financial strategy, including allocating one-third of the AI budget to risk management is essential for responsible and sustainable AI adoption.

Final Takeaway

Holistic AI governance is a strategic imperative that requires a deliberate balance of bold innovation and disciplined risk management. It is about more than just preventing downsides; it is about actively steering powerful technology toward achieving core business objectives. Leaders must shift from a reactive to a proactive stance, building the frameworks, teams, and financial commitments necessary to guide AI's integration into their organizations. By doing so, they can harness its transformative potential while ensuring a profitable, responsible, and sustainable future.

Learn More

To learn more about Dr. Lobana’s work—including her global advisory practice, research, and speaking engagements—please visit https://drjodielobana.com/. Her upcoming book, Holistic Governance of Artificial Intelligence, is now available for pre-order on Amazon  https://tinyurl.com/Book-Holistic-Governance-of-AI.You can also connect with her on https://www.linkedin.com/in/jodielobana/ to follow her insights, global updates, and thought leadership in AI governance.

Interviewer: 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. 

AI Iceberg: Tech Bubble Warnings, White-Collar Cuts, Deepfake Dilemma, and Canada's AI Strategy

‘Big Short’ Investor Bets Against AI Giants in Market Warning

Michael Burry, famed for predicting the 2008 financial crisis and immortalized in The Big Short, has disclosed new bearish positions through his hedge fund, Scion Asset Management. Burry has taken put options—investments that profit from a stock's decline—against two tech giants: Palantir and Nvidia. Despite Palantir’s strong earnings report and raised revenue outlook, its stock saw volatility due to valuation concerns. Nvidia also faced market jitters amid geopolitical tensions and pending earnings, particularly after former President Trump’s comments about limiting chip sales to China. Burry's move aligns with his recent warnings about an overheated market, echoing sentiments from other Wall Street leaders about inflated tech valuations. Known for his contrarian positions, Burry’s recent bets signal caution amid a tech-driven market rally fueled by AI hype (Source: Yahoo Finance).

• Contrarian Warning: Michael Burry is betting against Nvidia and Palantir, signaling concerns about a tech bubble.
• Market Volatility: Despite strong financials, Palantir's stock dropped due to valuation skepticism; Nvidia's dip was influenced by geopolitical factors.
• Broader Bearish Sentiment: Burry’s move aligns with a broader warning from major Wall Street voices about an impending market correction.

The Number One Sign You’re Watching an AI Video

As AI-generated videos flood social media, experts are warning that blurry, low-resolution footage is often the best clue you’re watching a fake. According to researchers like Hany Farid and Matthew Stamm, poor-quality videos are frequently used to mask telltale AI inconsistencies—such as unnatural skin textures or glitchy background movements—making them harder to detect. Many recent viral AI videos, from bouncing bunnies to dramatic subway romances, share a common trait: they look like they were filmed on outdated devices. While advanced models like OpenAI's Sora are improving, shorter clip lengths, pixelation, and intentional compression remain key signs. Experts argue we must shift from trusting visual “evidence” to verifying context and source—similar to how we assess text—because soon, visual cues may vanish entirely. The rise of these deceptively convincing clips signals a new era in digital literacy where provenance, not appearance, becomes the cornerstone of truth (Source: BBC).

• Low Quality, High Risk: Blurry, pixelated videos are a major red flag for AI fakes—they often hide subtle AI flaws.
• Short and Deceptive: AI-generated videos are usually brief due to high processing costs and a higher chance of mistakes in longer clips.
• Context Over Clarity: Experts urge people to stop trusting visuals alone—source and verification matter more than ever.

The $4 Trillion Warning: AI May Be Headed for a Historic Crash

Brian Merchant of Wired applies a scholarly framework to assess whether the AI industry is in a financial bubble—and concludes it likely is. Drawing on research by economists Brent Goldfarb and David A. Kirsch, who studied dozens of historical tech bubbles, Merchant finds AI checks every box for a classic speculative frenzy: high uncertainty, the dominance of “pure-play” companies like OpenAI and Nvidia, a surge of novice investors, and irresistible industry narratives promising everything from job automation to miracle cures. Unlike earlier technologies, AI’s ambiguity fuels investor enthusiasm instead of caution, while public and private markets pour unprecedented capital into ventures with unclear profit models. Nvidia, for example, now accounts for 8% of the total stock market value. Goldfarb ultimately rates AI at a full 8 out of 8 on the bubble-risk scale, likening today’s mania to the radio and aviation bubbles that preceded the 1929 crash. If AI fails to deliver on its sweeping promises, the fallout could be massive (Source: Wired).

• All Bubble Indicators Flashing: AI ranks highest on a tested framework for identifying tech bubbles—uncertainty, pure plays, novice investors, and grand narratives.
• Public at Risk: With firms like Nvidia heavily tied to public markets, a burst could affect everyday investors and retirement funds.
• Narrative-Driven Speculation: AI’s limitless promise has generated massive investment despite weak current returns, echoing past tech hype cycles.

White‑Collar Jobs Vanish as AI Reshapes the Office Landscape

Major U.S. companies—such as Amazon.com, Inc., United Parcel Service (UPS), and Target Corporation—are cutting tens of thousands of white‑collar roles as they adopt artificial intelligence and automation to streamline operations. Amazon announced plans to cut 14,000 corporate jobs (up to ~10 % of its white‑collar staff). UPS reduced its management workforce by about 14,000 positions over 22 months. These actions reflect a broader shift: traditionally secure white‑collar roles—even for experienced professionals and recent graduates—are becoming vulnerable. The wave of cuts is attributed in part to AI tools replacing or reducing the need for many tasks formerly done by higher‑paid office workers; at the same time, hiring remains stronger in blue‑collar or trade sectors. The changing landscape means intensified competition for fewer roles, and many workers are facing uncertainty about their careers (Source: The Wall Street Journal).

• White‑Collar Vulnerability: Even well‑educated office professionals are now at risk as AI enables firms to cut back on corporate staffing.
• Structural Shift in Jobs: While white‑collar hiring weakens, demand for trade and frontline roles is relatively stronger—signaling a change in which segments of the workforce are most secure.
• Increased Competition & Pressure: With fewer open roles and employers demanding more specific qualifications, both new grads and mid‑career workers face a tougher employment market.

Canada’s AI Crossroads: Sovereignty or Speed?

As AI infrastructure booms globally, Canada faces a critical decision: whether to deepen reliance on foreign tech giants like OpenAI or invest in sovereign, Canadian-controlled systems. While companies like OpenAI have proposed building AI data centers in Canada—attracted by the country’s clean energy supply—critics warn that such partnerships could threaten national digital sovereignty. Canadian data, from health records to mobility stats, is increasingly fueling foreign AI innovation and economic gains. Yet, the infrastructure to process and govern that data under Canadian law remains underdeveloped. The federal government has begun investing in domestic AI capabilities, but unless cloud and compute services are Canadian-owned and governed, experts argue that Canada will merely become a digital raw material supplier. Drawing parallels to the country’s historical resource exports, the article urges Canada to prioritize legal and economic control over its data to foster innovation and retain value at home (Source: Maclean’s).

• Sovereignty vs. Speed: Relying on U.S. tech firms for AI infrastructure risks ceding control over Canadian data and its economic value.
• Data as Digital Raw Material: Like lumber or oil, Canada’s data is being exported and monetized elsewhere while domestic innovation lags behind.
• A National Strategy Needed: Experts urge Canada to treat data governance and infrastructure as core to its economic and sovereign future.

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

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. 

MIT’s GenAI Freakout: A "95% Failure Rate" or 95 Years Worth of Productivity?

The now infamous MIT study has found that 95% of enterprise AI projects are generating zero returns. Like many statistics in the early days of any emerging technology, the truth is more complicated. When we look beyond the headlines, the story isn’t about the failure of GenAI — it’s about how we define success, what we expect from AI, and how employees are already rewriting the rules of enterprise adoption.

 

The Measurement Trap: Financial Metrics vs. Productivity Reality

The main challenge with the article was that it focused on financial returns, not the success of the actual technology. The article highlights the difficulty in quantifying GenAI’s “micro-productivity gains”. They cite the following from a Fortune 1000 procurement executive:

"If I buy a tool to help my team work faster, how do I quantify that impact? How do I justify it to my CEO when it won't directly move revenue or decrease measurable costs?"

For those of us who advocate for GenAI, we can empathize with the executive’s dilemma. I call this “micro-productivity gains” because, although saving minutes with GenAI is hard to quantify, these small efficiencies accumulate across the economy.

A great example is using GenAI to generate images.

Let’s say we save 5 minutes per image using GenAI instead of going on the “perfect pic for my presentation hunt”. Over a handful of images, we don’t see the gains. However, over 10 million images those time savings amount to 95 years of productivity!

AI Has Already Won—Where It Can

The article itself actually testifies to the significant success that the technology is bringing to the average knowledge worker. Remarkably, the article actually said the following:

"AI has already won the war for simple work."

The core argument of the article is that standard generative AI technology is not yet equipped to fully replace human workers. For example, only 10% of respondents would entrust multi-week client management projects to AI rather than to human colleagues.

This, however, is not surprising. Anyone with a paid subscription certainly knows that GenAI needs multiple iterations to get the desired output.

The idea that we have such high expectations of the technology – for it to replace a junior lawyer – is a function of hype, the automation bias, and science-fiction movies.

From BYOD to BYOAI? AI Governance in Crisis

Perhaps the most interesting finding is that 90% of employees use generative AI regularly, regardless of official policies. The study found that “almost every single person used an LLM in some form for their work”.

History does not repeat itself, but it certainly rhymes. This is not the first time that employees have tried to impose consumer tech on enterprise IT. With the ascent of the iPhone and Android in the early 2010s, workers demanded the IT department figure out a way to make their devices work with the corporate email server. This Bring Your Own Device (BYOD) movement ultimately displaced BlackBerry's enterprise dominance.

The advent of Shadow AI, as the report aptly termed this trend, is more problematic. Formerly, it would take someone quite technically adept to figure out how to get corporate data onto their device. With Shadow AI, it is only a matter of copy and paste. Consequently, AI adoption raises a range of considerations related to privacy/confidentiality, data leakage, and regulatory compliance that organizations must address.

Although Shadow AI speaks to the resounding success of the tech, it also speaks to the urgent need to get AI governance in place.

Beyond the Hype: What the Study Actually Reveals

Though the headlines were laser-focused on the lack of cash flow resulting from the money invested in AI, a more careful read of the article reveals the productivity boom resulting from the technology. It's startling to think that three years ago GenAI was non-existent to most. Today, we are disappointed with it because it can't replace a junior at a professional services firm.

That said, the article offered some valuable insights into what success with GenAI can look like—a topic I'll be unpacking in a future post.

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.