UWCISA’s 6 Tech Takeaways: Power, Partnerships, and Pressure in AI

When we look at the rivalry within the AI world, we see that competition is running along at least three axes at once: financial performance, geopolitical alignment, and architectural philosophy. 

Counterpoint Research data put Anthropic ahead of OpenAI in Q1 2026 LLM revenue (31.4% vs 29%) with roughly 134 million monthly users against OpenAI's 900 million. Anthropic's average revenue per active user sits near $16.20 against OpenAI's $2.20, which is what put the smaller player at the top of the revenue table. Microsoft's Copilot Cowork, built jointly with Anthropic, shows that even OpenAI's largest investor is hedging, while OpenAI itself is missing internal growth targets even as it commits hundreds of billions in data center spend. The geopolitical layer is just as active: Washington is pressing allies on Chinese model "distillation," Beijing has forced Meta to unwind its Manus deal, and DeepSeek V4 is shipping with explicit Huawei chip support. At the same time, Airbnb's customer service agent runs on Alibaba's Qwen because, in Brian Chesky's words, it is "fast and cheap," a choice that now sits in front of a House committee. 

The fault line that matters is no longer which American lab wins, but whether the open-source Chinese stack or the proprietary American stack becomes the default substrate for global enterprise AI. Once a default takes hold, switching costs and compliance regimes tend to lock it in for a decade.

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1. Washington Escalates AI Tensions with Global Warning on China

The U.S. State Department has launched a global diplomatic effort warning allies about alleged attempts by Chinese companies, including AI startup DeepSeek, to extract and replicate American artificial intelligence models. According to a diplomatic cable, U.S. officials are urging foreign governments to be cautious of “distillation” practices—where smaller AI systems are trained using outputs from more advanced models—arguing this could enable foreign firms to mimic U.S. technology at a fraction of the cost while potentially removing built-in safety measures. The accusations echo earlier warnings from OpenAI and the White House, though China has firmly denied the claims, calling them baseless and politically motivated. Meanwhile, DeepSeek continues to advance its technology, recently unveiling a new model compatible with Huawei chips, underscoring China’s growing independence in AI development.

Global Warning Issued: The U.S. is actively urging allies to be cautious about Chinese AI firms allegedly replicating American models.

Debate Over “Distillation”: The controversy centers on AI training techniques that may copy outputs from advanced systems at lower cost.

Rising Tech Tensions: The dispute risks escalating U.S.-China competition despite recent diplomatic easing.

(Source: Reuters)

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2. Power Play Intensifies: China Forces Meta to Unwind AI Deal

Meta is preparing to unwind its $2.5 billion acquisition of AI startup Manus after Chinese regulators blocked the deal on national security grounds, highlighting escalating control over cross-border AI technology. The startup, which has ties to China despite operating through Singapore, had already been partially integrated into Meta’s systems, making a reversal technically and financially complex. Beijing has reportedly ordered a full separation, including restoring Chinese assets and removing any transferred data or technology, with potential penalties if the process is incomplete. The move signals a broader strategy by China to retain AI capabilities within its borders and limit foreign access, even at the cost of discouraging international investment.

Deal Reversal: Meta may be forced to undo a major AI acquisition due to Chinese national security concerns.

Data Sovereignty: China is tightening control over AI technology and cross-border transfers.

Global Fragmentation: Tech companies face increasing risk from geopolitical barriers to deals and partnerships.

(Source: The Wall Street Journal)

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3. Why DeepSeek’s V4 Could Reshape the AI Landscape

DeepSeek’s newly released V4 model represents a significant step forward in open-source artificial intelligence, offering performance comparable to leading proprietary systems at a fraction of the cost. The model introduces major technical improvements, including a massive one-million-token context window and a more efficient attention mechanism that reduces computing and memory demands while handling large-scale data. Available in two versions—V4-Pro for complex tasks and V4-Flash for faster, cheaper deployment—it is positioned as one of the most powerful open-source models to date, particularly in coding and technical problem-solving. Beyond performance, V4 highlights China’s broader push for AI independence, as it is optimized for domestic chips like Huawei’s Ascend.

Open-Source Breakthrough: V4 delivers top-tier AI performance at significantly lower costs, making advanced AI more accessible.

Efficiency Innovation: Its new architecture dramatically improves memory use and enables processing of extremely large inputs.

Strategic Shift: The model supports Chinese-made chips, signaling a move toward technological independence from U.S. hardware.

(Source: MIT Technology Review)

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4. Cracks Emerge in OpenAI’s High-Stakes Race for AI Dominance

OpenAI is facing mounting internal concerns after missing key revenue and user growth targets, raising questions about its aggressive spending strategy as it eyes a potential IPO. Executives, including CFO Sarah Friar, have reportedly warned that slowing growth could make it difficult to sustain the company’s enormous commitments to data center infrastructure, which total hundreds of billions of dollars. While CEO Sam Altman continues to push for securing vast computing resources to fuel future AI demand, some board members are urging greater financial discipline. The company has also faced increased competition from rivals like Google and Anthropic, impacting revenue and market share.

Missed Targets: OpenAI fell short on both user growth and revenue expectations, raising internal concerns.

Spending Pressure: Massive data center investments are under scrutiny as growth slows.

IPO Uncertainty: Financial discipline and operational readiness are becoming critical ahead of a potential public listing.

(Source: The Wall Street Journal)

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5. Anthropic’s Mythos AI Sparks Fears of a New Cybersecurity Era

Anthropic’s latest AI model, Mythos, is raising significant concern across the tech industry due to its unprecedented cybersecurity capabilities and advanced reasoning skills. Unlike typical AI releases, the company has chosen not to make Mythos publicly available, citing risks that its powerful ability to detect and potentially exploit software vulnerabilities could be misused. Instead, access is being limited to cybersecurity experts and major organizations through a controlled initiative aimed at identifying and fixing system weaknesses. While these capabilities could greatly enhance defensive cybersecurity, experts warn they may also enable more sophisticated cyberattacks if the technology falls into the wrong hands.

Restricted Release: Anthropic is limiting access to Mythos due to concerns over misuse and security risks.

Powerful Capabilities: The model can detect deep vulnerabilities and demonstrates highly advanced reasoning.

Security Trade-Off: While useful for defense, Mythos could enable more dangerous cyberattacks if misused.

(Source: MSN)

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6. Microsoft and Anthropic Signal a New Era of AI-Powered Work

Microsoft’s Copilot Co-Work initiative reflects a broader shift in the AI industry toward deeply integrated, enterprise-ready systems—an approach that aligns closely with partners like Anthropic. While Copilot acts as an embedded “co-worker” across Microsoft 365, enabling real-time collaboration, automation, and decision support, its evolution also highlights the importance of combining powerful AI capabilities with safety and reliability. Microsoft’s partnership with Anthropic underscores a growing emphasis on responsible deployment as AI systems become more autonomous in the workplace.

Strategic Alignment: Microsoft’s AI direction complements Anthropic’s focus on safe, controlled deployment.

AI as Infrastructure: Copilot Co-Work embeds AI deeply into everyday workflows and collaboration.

Partnership-Driven Future: Major AI advancements are increasingly shaped by alliances between leading firms.

(Source: Microsoft)

UWCISA's 5 Tech Takeaways: Policy, Platforms, and Power Plays

In this week's 5 Tech Takeaways, we look at the ongoing legal battle between Amazon and Perplexity.

A federal appeals court recently granted Perplexity an administrative stay, pausing an injunction that would have blocked its AI shopping agent from operating on Amazon. Amazon says Perplexity's Comet browser accessed customer accounts without authorization. Perplexity argues the lawsuit is really about eliminating a competitor to Amazon's own AI shopping tools.

Does this sound like a standard corporate dispute? Well, it's actually not.

It's another chapter in the long history of powerful incumbents using legal/regulatory means to decide which innovations get to live and which get buried. In an earlier post, I wrote about how a broader view of regulation helps appreciate how market incumbents leverage courts, legislation, and other industry-related chokepoints to potentially throttle innovation from seeing the light of day.

David Sarnoff of RCA is the textbook case. His former friend Edwin Armstrong invented FM radio, a technology so advanced it could have been transmitting data like faxes back in the 1930s. Sarnoff's response wasn't to compete on the merits. Instead, he used RCA's market dominance and patent warfare to bury FM, protecting his AM radio empire and clearing the runway for television. Armstrong spent years fighting RCA in the courts over patent rights and died without ever seeing FM get its fair shot. AT&T played a similar game from an even more powerful position. As a regulated monopoly, they ultimately decided what innovations saw the light of day. In 1934, they blocked the answering machine, not because the technology didn't work, but because they feared that the ability to record conversations would scare off business customers and cannibalize their telephone service offering.

Fast forward to today and the pattern hasn't changed, just the players. Amazon sued Perplexity, and a federal judge drew a distinction that could define the era of AI agents: Comet accessed Amazon accounts with the user's permission, but without Amazon's authorization. By blocking outside AI agents and promoting its own shopping assistant Rufus, Amazon is building a walled garden where it controls the AI, the data, and the advertising revenue.

More broadly, the legal standoff illustrates that innovation is not a purely tech play. When Amazon argues that platform authorization trumps user permission, they are effectively saying that the consumer's choice of tools is secondary to the platform's right to control its ecosystem. It is the decisions made by market makers, and their ability to influence the organs of society, that ultimately determine how innovation unfolds, not the scrappy entrepreneur.

New Federal AI Framework Aims to Override State-Level Rules

The Trump administration has introduced a national artificial intelligence policy framework aimed at creating a unified regulatory approach across the United States. The proposal would establish consistent safety, security, and operational standards for AI technologies, including rules around child protection, data center energy use, and intellectual property rights. A central objective is to stop individual states from creating their own AI regulations, which industry leaders argue would produce a fragmented system that could slow innovation and weaken the United States in its competition with China. The administration now wants Congress to convert the framework into law, though deep partisan divisions could make that difficult. (Source: CNBC)

• National standard push: The administration wants one federal AI framework to replace a patchwork of state laws.
• Balancing innovation and safety: The proposal combines pro-growth goals with guardrails on child safety, energy use, and intellectual property.
• Political hurdles ahead: Even with White House support, turning the framework into law may prove difficult in a divided Congress.

Canadian Legal Tech Firm Clio Fights Off AI Giants and U.S. Pressure

Vancouver-based legal tech company Clio is trying to cement its place as a global AI leader while resisting pressure to move south of the border. CEO Jack Newton sees artificial intelligence as both an enormous opportunity and a growing threat as companies like OpenAI and Anthropic expand deeper into legal workflows. Clio has responded with major acquisitions, including its $1 billion purchase of vLex, and by leaning into what Newton describes as its biggest competitive advantage: proprietary legal data. Despite market volatility and rising investor scrutiny around SaaS businesses in the AI era, Clio has grown into one of Canada’s most valuable private tech firms and continues to expand internationally while keeping its headquarters in British Columbia. (Source: Financial Post)

• AI as both opportunity and threat: Clio is using AI to expand, even as larger AI firms threaten to disrupt its market.
• Data as the moat: The company believes its legal data ecosystem gives it a durable competitive edge.
• A Canadian growth story: Clio is expanding aggressively abroad while deliberately choosing to remain headquartered in Canada.

Amazon vs. Perplexity: Legal Clash Over AI Shopping Agents Intensifies

Image Prompt: Make a photorealisic image of a sprawling city at night seen from above, with some neighborhoods lit by organized grid lighting and others flickering with scattered, mismatched neon signs and unregulated wiring. no people, no animals. Model: Nano Banana 2 via Poe.

A U.S. appeals court has temporarily allowed Perplexity AI to continue running its AI-powered shopping agents on Amazon, pausing an earlier court order that blocked the product. Amazon argues that Perplexity’s tools improperly accessed private customer accounts and masked automated behavior, creating security concerns. Perplexity denies the allegations and says the lawsuit is really an attempt to suppress competition and restrict how consumers use AI tools online. The court’s temporary stay gives Perplexity breathing room while the broader legal dispute continues, and the outcome could shape how AI agents are allowed to interact with major digital platforms in the future. (Source: Reuters)

• A fight over AI platform access: Amazon and Perplexity are battling over whether AI shopping agents can operate on a major marketplace.
• A temporary win for Perplexity: The appeals court pause allows the company to keep its tool active for now.
• Broader implications for AI agents: The case could influence future rules for how autonomous AI tools interact with online services

For more context, see WSJ's article on Amazon's original win against Perplexity. 

OpenAI Unveils Plan for All-in-One AI “Superapp”

OpenAI is planning a desktop “superapp” that would bring together ChatGPT, its Codex coding platform, and a browser into one product. The move marks a shift away from a scattered collection of standalone offerings and toward a more unified user experience, as the company tries to sharpen its product focus and respond to stronger competition from Anthropic. OpenAI says the new app will center on “agentic” capabilities, allowing AI systems to perform tasks more autonomously on behalf of users, from coding to data analysis. The strategy also reflects the company’s growing attention to enterprise customers and productivity use cases. (Source: Wall Street Journal)

• One app, not many: OpenAI is consolidating key products into a single desktop experience.
• Agentic AI takes center stage: The new platform is designed to support more autonomous AI task execution.
• Enterprise pressure is rising: The product shift reflects mounting competition and growing demand from business users.

AI Adoption Surges as Companies Struggle With Governance

A new LexisNexis report finds that generative AI has quickly moved from experimentation to daily use across professional workplaces, but governance and oversight have not kept pace. Many employees are using AI tools without formal approval, and large numbers still lack clear policies or sufficient training. At the same time, professionals say they are increasingly confident in using AI, even as many organizations struggle to explain how internal AI systems work. The report argues that human oversight remains essential and outlines practical steps for leaders to scale AI responsibly, including stronger governance councils, clearer policies, vetted tools, and better validation processes. (Source: LexisNexis)

• Usage is accelerating faster than oversight: AI adoption is growing quickly, but governance structures are lagging behind.
• Human validation still matters: Most professionals believe people should remain actively involved in AI-driven workflows.
• Governance is the scaling challenge: Organizations need clearer rules, training, and controls to expand AI responsibly.

Author: Malik D. CPA, CA, CISA. This post was written with the assistance of an AI language model. 

UWCISA's 5 Tech Takeaways: Who Pays the Real Cost of the AI Boom?

Prompt: "Make a Photorealistic landscape of industrial smokestacks in the far distance emitting white vapor, reflected perfectly in a calm river in the foreground, surrounding wetlands and reeds, early dawn pink and grey sky, environmental contrast between nature and industry, wide-angle composition, ultra-realistic detail"; Model: Gemini, Mode: Cinematic

Every story in this week's roundup shares a common tension: speed versus control. AI-generated code is flooding review queues faster than teams can check it. Startups are getting cheaper to build, but investors aren't sure where that leaves them. Legal professionals know AI works, but adoption still lags behind the hype. And data centers are going up faster than the grid can handle them cleanly. The pace of AI development keeps accelerating, and the systems meant to govern it are struggling to keep up.

Venture Capital’s Next Disruption May Be Itself

WIRED examines whether venture capitalists’ biggest bet—AI—could end up reshaping or even undermining venture capital itself. The piece centers on ADIN, an “Autonomous Deal Investing Network” that uses AI agents to evaluate startups, perform diligence, estimate markets, and recommend valuations in a fraction of the time human analysts need. While many investors argue that early-stage investing still depends on intuition, networks, and judgment that AI cannot fully replicate, others see AI as a “Moneyball” moment for venture capital, where data-driven systems outperform gut feel. The article also argues that the greater threat may not be AI replacing investors directly, but AI making startups cheaper to build, reducing founders’ need for large venture checks and potentially eroding the business model that modern VC firms depend on. (Source: WIRED)

• AI as investor: AI platforms like ADIN are already analyzing startup pitches, surfacing risks, and recommending investments faster than traditional VC workflows.
• Human edge under pressure: Many investors still believe founder judgment, trust, and taste remain difficult for AI to replicate in early-stage deals.
• The bigger disruption: AI may hurt venture capital most by making software startups far cheaper to build, shrinking demand for large VC funding rounds.

OpenAI’s Next Enterprise Bet: Safer Agents

OpenAI announced plans to acquire Promptfoo, a security platform focused on testing and evaluating AI systems, in order to strengthen OpenAI Frontier, its enterprise platform for building and operating AI coworkers. The deal reflects growing demand from enterprises for better tools to identify vulnerabilities, test agent behavior, enforce compliance, and maintain oversight as AI agents become embedded in business workflows. OpenAI says Promptfoo’s technology will help Frontier offer built-in red-teaming, security checks, governance, and reporting, while the open-source Promptfoo project will continue. The acquisition signals how quickly enterprise AI is shifting from experimentation toward operational requirements like trust, accountability, and risk management. (Source: OpenAI)

• Security becomes core infrastructure: OpenAI is treating evaluation, red-teaming, and compliance as foundational features for enterprise AI deployment.
• Enterprise pressure is rising: As AI coworkers move into real business processes, companies need stronger ways to test behavior and document risks.
• Open source plus platform integration: Promptfoo’s open-source tools will continue while its capabilities are folded into OpenAI Frontier.

Claude Code Adds a Second Pair of AI Eyes

TechCrunch reports that Anthropic has launched Code Review inside Claude Code, an AI-powered reviewer aimed at helping enterprises manage the surge of pull requests created by AI-assisted coding. The tool analyzes code submitted through GitHub, flags logic issues, explains its reasoning, suggests fixes, and prioritizes findings by severity. Anthropic is positioning the product as a response to a new bottleneck: while AI coding tools dramatically accelerate software creation, they also produce more bugs, risks, and poorly understood code that still must be reviewed before shipping. With pricing estimated at $15 to $25 per review, Anthropic is targeting large enterprise customers already seeing massive gains in code output—and mounting pressure to maintain quality. (Source: TechCrunch)

• AI creates a new bottleneck: Faster code generation is increasing the volume of pull requests and making review the next major constraint.
• Focus on useful feedback: Anthropic says the tool prioritizes logical errors and actionable fixes rather than nitpicky style comments.
• Enterprise-first strategy: Code Review is aimed at large organizations that need scalable oversight for growing amounts of AI-generated software.

Lawyers Are Asking Two Big Questions About AI

Business Insider’s dispatch from Legalweek 2026 shows an industry torn between AI hype and adoption anxiety. While legal-tech vendors aggressively pitched AI agents that can draft, review, and automate legal workflows, many lawyers remain hesitant to use the tools at all. Conference attendees repeatedly returned to two concerns: how to persuade lawyers to adopt AI, and whether failing to use effective AI tools could eventually look like malpractice. The article argues that skepticism is driven by fears over job loss, billing-model disruption, and inadequate training, even as clients increasingly demand faster and cheaper legal services. For legal tech startups and law firms alike, the stakes are high: billions are riding on whether lawyers move from curiosity to everyday use. (Source: Business Insider)

• Adoption remains uneven: Even with strong AI use cases like contract review, many lawyers still are not using automation tools regularly.
• Fear is slowing change: Concerns about job security, hourly billing, and lack of confidence with the tools are holding adoption back.
• Client expectations may force the issue: As corporate clients demand efficiency, firms may face pressure to treat AI use as part of competent representation.

The Hidden Cost of the AI Boom

Prompt: "Make a Photorealistic dramatic landscape of a massive thunderstorm rolling over a scorched golden grassland, dark storm clouds with visible lightning in the distance, dry cracked foreground, tension between destruction and renewal, wide-angle, natural lighting, ultra-high detail, National Geographic style"; Model: Gemini, Mode: Cinematic

In this sweeping Atlantic feature, Matteo Wong explores the physical and environmental costs of the AI boom through the lens of giant data centers, including xAI’s Colossus facility in Memphis and the planned restart of Three Mile Island’s Unit One reactor. The article argues that AI’s rapid growth is reshaping not just software and work, but also electricity grids, local air quality, water use, and climate strategy. Because AI data centers require enormous amounts of power and cooling, tech companies are increasingly turning to natural gas and other fossil-fuel sources even as they also invest in nuclear and renewable options. The result is a race between the speed of AI deployment and the slower timelines of clean-energy infrastructure, with frontline communities often bearing the immediate environmental burden. (Source: The Atlantic)

• AI’s footprint is physical: The AI boom is driving massive new demand for electricity, water, land, and industrial infrastructure.
• Fossil fuels are filling the gap: Because clean energy cannot be deployed fast enough, many new AI facilities are leaning on natural gas in the near term.
• Communities feel the cost first: Residents near large data centers may face worsening pollution and health concerns long before AI’s promised benefits arrive.

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 Rift That Built a Rival: Why Dario Amodei Left OpenAI

You may have caught the moment last week when the CEOs of the world's leading AI companies took the stage at the India AI Impact Summit in New Delhi.Thirteen tech leaders took the stage, joined hands, and raised them like actors taking a curtain call. Twelve of them obliged. Altman and Anthropic's Dario Amodei, standing next to each other, did not. Both put up a fist instead.

Source: https://youtube.com/shorts/vS68BsFWIqY?si=UWIB1-rYJ4yIiEvK

 

The clip went viral within hours, and for good reason. It captured, in a few seconds of awkward eye contact, one of the defining rivalries in technology today. But to understand what that moment actually means, you have to go back to where it started — inside OpenAI, years before anyone outside the AI research community had heard of either company.

Two Men, One Lab, a Diverging Vision

I first came across Dario Amodei in the pages of Brian Christian's The Alignment Problem — a serious and accessible book on the gap between what we ask AI systems to do and what they actually end up doing. Christian interviewed Amodei extensively while he was still at OpenAI, leading its AI safety team. One episode in the book stands out.

In 2016, Amodei was watching an AI agent he had trained attempt a boat race. The boat was not racing. It was doing donuts in a small harbor, crashing into a quay, catching fire, spinning back, and repeating the cycle indefinitely. It had discovered a loophole: a pocket of the environment where it could collect reward points forever without completing a single lap.

"And I was looking at it, and I was like, 'This boat is, like, going around in circles. Like, what in the world is going on?!'" — Amodei, quoted in Christian (2020, p. 8)

Christian describes what happened next: Amodei had made what he calls "the oldest mistake in the book — rewarding A, while hoping for B" (Christian, 2020, p. 9). The machine did exactly what it was told. It simply was not told the right thing.

When critics pointed this out, Amodei did not deflect:

"People have criticized it by saying, 'Of course, you get what you asked for.' It's like, 'You weren't optimizing for finishing the race.' And my response to that is, Well—" He pauses. "That's true." — Amodei, quoted in Christian (2020, p. 9)

For Christian, the boat race is a parable about the entire alignment problem — the challenge of specifying human values precisely enough that a powerful AI system actually serves them. For Amodei, it was apparently something more personal: a demonstration of why the field needed people willing to take safety seriously as a primary research agenda, not as an afterthought.

"The real game he and his fellow researchers are playing isn't to try to win boat races; it's to try to get increasingly general-purpose AI systems to do what we want, particularly when what we want — and what we don't want — is difficult to state directly or completely." — Christian (2020, p. 9)

This was the intellectual foundation Amodei brought with him when he left.

Why He Left

The official record on Amodei's departure from OpenAI is thinner than you might expect for such a consequential event. What we know comes mainly from his own public statements.

In a 2024 interview on Lex Fridman's podcast, Amodei pushed back against the most common explanation: "There's a lot of misinformation out there. People say we left because we didn't like the deal with Microsoft. False." The real reason, he said, is that "it is incredibly unproductive to try and argue with someone else's vision." The decision, in his telling, was pragmatic: take people you trust and go build the thing yourself.

In a 2023 interview with Fortune, he described the belief system that drove the split: "There was a group of us within OpenAI, that in the wake of making GPT-2 and GPT-3, had a kind of very strong focus belief in two things. One was the idea that if you pour more compute into these models, they'll get better and better and that there's almost no end to this. And the second was the idea that you needed something in addition to just scaling the models up, which is alignment or safety."

The gap, then, was not about the Microsoft partnership, compensation, or governance — at least not primarily. It was about what the company should be optimizing for. Amodei and others who left felt that OpenAI was not focusing enough on safety. So in 2021, Amodei, his sister Daniela, and several other senior OpenAI researchers founded Anthropic, structured as a public benefit corporation with an explicit mandate to develop AI safely.

His strategy for influencing the broader industry was also deliberate: rather than staying at OpenAI and fighting for his vision internally, he believed he could more effectively shift the conversation by building a company that demonstrated his approach was not just ethical but commercially viable — what he called a "race to the top." "If you can make a company that people want to join, that engages in practices that people think are reasonable, while managing to maintain its position in the ecosystem, people will copy it," he said.

That thesis remains unproven. Amodei has since acknowledged that balancing safety and profit is harder in practice than in theory: "We're under an incredible amount of commercial pressure and make it even harder for ourselves because we have all this safety stuff we do."

Capital Follows Vision — When You Let It

What Amodei did is rarer than it looks. Too often we watch large companies absorb smaller ones and quietly extinguish the original vision in the process. The entrepreneur Marc Lore lived this firsthand. When Amazon acquired his company Quidsi — the parent of Diapers.com — in 2011, Lore did not walk away satisfied. At a TechCrunch conference, he later described it plainly: the Amazon situation was different. It was forced. "We did not want to sell," he said, calling it "upsetting, because we sold out." He went on to found Jet.com, which he sold willingly to Walmart in 2016 on terms he controlled.

Most listeners find Lore's Amazon regret puzzling. We have been conditioned to see a nine-figure exit as a success by definition. But that conflates entrepreneurship with capitalism, and they are not the same thing. Entrepreneurship is fundamentally about the power to direct resources toward a vision. Capitalism is simply the system through which capital is allocated. The two can align — but they often do not.

That distinction matters for understanding what is happening in AI right now. Amodei was not just a disgruntled employee. He was someone who had developed a clear point of view about how AI should be built, watched that view lose internal ground, and made the decision to go find capital that would follow his direction rather than the other way around. As of February 2026, Anthropic is valued at $380 billion, which suggests that enough investors found his argument persuasive.

Whether that is enough to win is another question entirely. Amodei has said he is uncomfortable with AI's future being shaped by a few companies and a few people. He just made sure he would be one of them. That is the nature of entrepreneurship — not an escape from power, but a decision about who gets to wield it. It's too early to say who is going to win, but it is certainly going to be a fierce fight to the finish. Those fists in New Delhi said as much.

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References

Christian, B. (2020). The alignment problem: Machine learning and human values. W. W. Norton.

Sherry, B. (2024, November 13). Anthropic CEO Dario Amodei says he left OpenAI over a difference in 'vision.' Inc. https://www.inc.com/ben-sherry/anthropic-ceo-dario-amodei-says-he-left-openai-over-a-difference-in-vision/91018229

Quiroz-Gutierrez, M. (2026, February 17). Anthropic was supposed to be a 'safe' alternative to OpenAI, but CEO Dario Amodei admits his company struggles to balance safety with profits. Fortune. https://fortune.com/2026/02/17/anthropic-ceo-dario-amodei-balancing-safety-commercial-pressure-ai-race-openai/

Associated Press. (2026, February 19). Modi's AI summit turns awkward as tech leaders Sam Altman and Dario Amodei dodge contact. AP News. https://apnews.com/article/altman-amodei-india-ai-summit-photo-9067be4a101fcc710b09e297f4879c01

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. 

OpenAI's Ad Gambit: A Stopgap on the Road to Agentic Commerce?

OpenAI's announcement that it would begin testing ads on ChatGPT marks a pivotal inflection point for the AI giant. According to Business Insider, Evercore ISI analyst Mark Mahaney projects that advertising could become a $25 billion annual business for OpenAI by 2030. That sounds bullish until you look beneath the surface.

The reality is stark: OpenAI is hemorrhaging money at a pace rarely seen in tech history. The company's burn rate has reached approximately $9 billion annually, and it expects cumulative cash burn of $115 billion through 2029.  

For context, competitor Anthropic expects to break even by 2028, with its burn rate projected to drop to roughly one-third of revenue in 2026 and just 9% by 2027. OpenAI, by contrast, expects its burn rate to remain at 57% in 2026 and 2027. The company expects to burn through roughly 14 times as much cash as Anthropic before turning a profit in 2030.

This isn't a company leisurely exploring new revenue streams. This is a company that needs cash, and needs it now. The ads announcement is less a strategic pivot than an acknowledgment of financial gravity.

The Google Irony

The irony here is worth noting: OpenAI is not the first company dragged into advertising against its original philosophy. The original reluctant advertiser? Google itself.

In their 1998 Stanford research paper, "The Anatomy of a Large-Scale Hypertextual Web Search Engine," Larry Page and Sergey Brin explicitly warned that "advertising funded search engines will be inherently biased towards the advertisers and away from the needs of the consumers." They argued that superior search would actually reduce the need for ads. Yet Google became the most successful advertising company in history, generating nearly $300 billion in ad revenue in 2025 from Search and YouTube alone.

Now OpenAI finds itself in the same position: a company built on the promise of intelligence-first interactions, contemplating whether to litter that experience with sponsored content.

Clayton Christensen's Framework

This brings us to what Clayton Christensen termed "The Innovator's Dilemma." In his 1997 work, Christensen demonstrated how successful companies can do everything "right" and still lose their market leadership. The core insight: established firms optimize for their existing customers and revenue streams, making them vulnerable to disruptive technologies that initially seem inferior or irrelevant.

Google is living this dilemma in real time. The company could have beaten OpenAI to the generative AI punch. It had the talent, the compute, and the research (Transformer architecture originated at Google, after all). But Google was reticent to test generative technology aggressively because doing so would cannibalize its search advertising revenue. Why encourage users to get answers directly from an AI when you profit from them clicking through multiple search results?

This hesitation created the opening OpenAI exploited. Although Google is playing catch-up, shareholders cannot fault the company from making hay when the sun shined - cashing in on ad-driven search was the only rational play in pre-GenAI world. Now is a different story. Google has launched  subscription services like Google AI Pro at $26.99/month and Google AI Ultra at $339.99/month (CAD). The fact that Google is experimenting with subscription models at all suggests the company recognizes its advertising cash cow may have a finite lifespan.

The Streaming Precedent

OpenAI and Google aren't alone in their reluctant embrace of advertising. The streaming industry provides a cautionary tale.

Netflix, which famously built its brand on ad-free viewing, launched its ad-supported tier in late 2022. By 2025, the company generated over $1.5 billion in advertising revenue and projects that figure to double to approximately $3 billion in 2026. Amazon Prime Video followed suit in January 2024, instantly becoming the largest ad-supported subscription streaming service in the world. By late 2025, Prime Video reached 315 million monthly ad-supported viewers globally.

The pattern is clear: companies that promised premium, uninterrupted experiences eventually succumb to the siren song of advertising revenue. The question isn't whether ads compromise the user experience. The question is whether the alternative (running out of cash) is worse.

Beyond Ads—The Agentic Commerce Model

Advertising may be OpenAI's stopgap solution, but it is unlikely to be its endgame.

The Walmart Signal

In October 2025, Walmart announced a partnership with OpenAI to create what both companies call "agentic commerce." The collaboration allows customers to shop directly through ChatGPT using Instant Checkout. As Walmart CEO Doug McMillon put it: "For many years now, eCommerce shopping experiences have consisted of a search bar and a long list of item responses. That is about to change."

This is the real signal. OpenAI isn't just thinking about displaying ads alongside chat responses. It's positioning itself as an intermediary between consumers and retailers, a position that carries far more revenue potential than advertising.

The "Costco Model" for AI

Consider what happens as agentic AI matures. You might tell ChatGPT: "Order my usual groceries from Walmart for pickup on Saturday, but check if there are any good deals on chicken this week. And remember, I'm still doing keto."

In this scenario, OpenAI becomes something like a Costco for the AI age: a membership-based service where you pay for access to automated, intelligent commerce. The value proposition isn't just the AI itself but the integrations, the reliability, the human-in-the-loop quality assurance during the early phases, and eventually, the pure automation.

This model offers multiple revenue streams:

• Consumer memberships: Users pay a monthly fee for access to premium agentic services
• Merchant fees: Retailers like Walmart pay for preferred integration status
• Transaction fees: A small percentage of each completed purchase

However, the Costco analogy has limits. Costco's model derives 73% of its gross profit from membership fees, which work because the company leverages massive purchasing power to negotiate wholesale pricing from suppliers. OpenAI would lack this kind of supplier leverage; its value would come from convenience and AI intelligence rather than from negotiating better prices. A more accurate framing might be that OpenAI would function as a digital concierge service with membership economics, not a wholesale negotiator.

The Third Wave of Commerce

We've seen commerce evolve from physical stores to e-commerce. Agentic AI represents a third wave where computation doesn't just facilitate your purchase, it makes the purchase for you. OpenAI and Anthropic could bypass both Amazon's retail dominance and Google's search dominance simultaneously by becoming the trusted intermediary between consumers and merchants.

The real money isn't in showing you ads for products. It's in being the system that handles your entire purchasing relationship with the world.

Conclusion

OpenAI's move into advertising is understandable given its current burn rate, but it should be viewed as a bridge, not a destination. The company needs cash to survive long enough to build something more durable. That something is likely agentic commerce: a membership-based model where AI companies act as trusted intermediaries, guaranteeing accuracy, handling customer service, and eventually automating the entire consumer-merchant relationship.

Google warned against ad-funded search in 1998 and became an advertising colossus. Now OpenAI, built on the promise of direct intelligence, may follow the same path, at least temporarily.

It's also worth noting that we're in the early phases of this transition, and major retailers are hedging their bets. In January 2026, Walmart announced a similar partnership with Google, allowing customers to shop directly through the Gemini app. This suggests that even as agentic commerce takes shape, the ultimate winners remain unclear, and the largest retailers are positioning themselves to work with whichever AI platform prevails.

The question for OpenAI isn't whether ads will generate revenue. The question is whether OpenAI can execute fast enough on the agentic commerce vision before burning through its capital or compromising the user experience that made ChatGPT dominant in the first place.

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. 

UWCISA's 5 Tech Takeaways: Big Bets, Quiet Progress, and What Comes Next

A key question is on everyone's mind: how are companies using GenAI? 

WSJ attempts to answer this question (see link below). Here's what I found relevant from the article:

Automating existing workflows:  Companies are using AI to speed up processes that were already being streamlined with older automation tools. The big difference now is that AI can handle "unstructured data"—meaning it can read and extract information from things like emails, Word documents, and PDFs that older software couldn't easily process. This lets companies connect messy, human-written content to their existing automated systems.

Summarizing content: One of the most common uses is having AI condense large amounts of text—reports, documents, meeting notes, research—into shorter summaries. All this is widespread it's "not that exciting."

Research tasks: AI is handling what the reporters call "really boring research"—the kind of tedious information-gathering that used to eat up employee time. I've found DeepResearch to be an excellent tool to do a first pass at an exploratory research task. At a minimum, you get a list of links that can be a good starting. 

Customer service: AI is answering customer calls and powering chatbots. The reporters note that while the technology has existed for years, companies were initially afraid to let AI talk directly to customers (worried about hallucinations, mistakes, or even hacking incidents where chatbots were manipulated into saying inappropriate things). For what can go wrong, check out Air Canada's experience. 

Writing code: Developers are using tools like GitHub Copilot and Claude Code to help write software. One reporter mentioned that companies are rethinking hiring because of this—instead of hiring 100 engineers, they might only need five if AI handles some of the coding work.

AI at Work: Big Promises, Small but Steady Gains

Despite bold claims from executives, corporate AI adoption is often quieter and more incremental than transformative. Companies are primarily using AI to automate existing workflows, summarize content, and support customer service rather than reinventing entire operations. While interest in autonomous “agentic” AI is growing, most organizations remain cautious, keeping humans in the loop due to concerns over reliability and trust. Leaders remain optimistic about AI’s long-term value, focusing on efficiency gains and future competitiveness rather than immediate financial returns.

Key Takeaways

• Most AI gains are incremental: Companies are seeing steady improvements in productivity without dramatic operational overhauls.
• Trust limits autonomy: Concerns about errors and hallucinations are preventing widespread deployment of fully autonomous AI agents.
• Leadership drives success: Organizations where top executives actively champion AI tend to see deeper and more effective adoption.

(Source: Wall Street Journal)

Inside Satya Nadella’s Plan to Reinvent Microsoft for the AI Era

Microsoft CEO Satya Nadella has launched a sweeping overhaul of the company’s senior leadership as he pushes to strengthen Microsoft’s artificial intelligence strategy beyond its once-exclusive partnership with OpenAI. Facing intensifying competition from rivals such as Alphabet and Amazon, Nadella has made high-profile external hires, reshuffled internal responsibilities, and adopted a more hands-on, “founder mode” leadership style to accelerate innovation. These changes aim to speed the development of Microsoft’s own AI models, coding tools, and applications while cutting internal bureaucracy. The move follows a restructuring of Microsoft’s relationship with OpenAI that will gradually reduce Microsoft’s privileged access to its partner’s models, forcing the company to build a more independent AI future.

Key Takeaways

• Leadership shake-up to boost speed: Nadella has restructured Microsoft’s senior leadership to reduce bureaucracy and accelerate decision-making around AI development.
• Preparing for life beyond OpenAI: With exclusive access to OpenAI’s models set to fade over time, Microsoft is investing heavily in building its own AI models and internal capabilities.
• Competition driving urgency: Increased pressure from rivals and AI start-ups is forcing Microsoft to move faster and rethink how it executes its AI strategy.

(Source: Financial Times)

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No Slowdown Ahead: Why AI’s Momentum Will Carry Into 2026

The rapid expansion of artificial intelligence shows no signs of slowing as 2026 approaches, according to a Dalhousie University computer science professor. AI has become deeply integrated into everyday life, powering tools such as weather forecasting, medical diagnostics, and decision-support systems while dramatically reducing computational costs. However, the growing sophistication of AI also brings risks, including more advanced phishing attacks and potential psychological effects on users. Experts say stronger regulation and widespread education will be essential as AI becomes more personalized and embedded across society.

Key Takeaways

• AI adoption will continue accelerating: Experts expect AI tools to become more powerful, specialized, and widely used throughout 2026.
• Benefits are tangible and growing: AI is already delivering measurable improvements in efficiency, accuracy, and cost reduction across multiple industries.
• Risks must be addressed: Increased use of AI raises concerns around cybersecurity, mental health, and misinformation that require regulation and education.

(Source: BNN Bloomberg)

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Meta’s AI Buying Spree Continues With Manus Acquisition

Meta Platforms has acquired Manus, a Singapore-based developer of general-purpose AI agents, as part of its aggressive push to expand automation across consumer and enterprise products. Manus experienced rapid growth after launching its AI agent earlier this year, claiming more than $100 million in annualized revenue within eight months. Meta plans to integrate Manus’s technology into products such as its Meta AI assistant while allowing the company to continue operating independently. The deal highlights Meta’s broader strategy of acquiring AI start-ups to secure talent and technology amid intensifying competition.

Key Takeaways

• Meta is betting big on AI agents: The acquisition strengthens Meta’s push to automate complex tasks across its consumer and business products.
• Manus scaled at extraordinary speed: The start-up’s rapid revenue growth underscores strong demand for AI agent technology.
• Talent acquisition remains critical: Meta continues to use acquisitions to secure AI expertise and stay competitive in the AI arms race.

(Source: CNBC)

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Inside Nvidia’s $20 Billion Groq Deal — And Who Gets Paid

A complex $20 billion agreement between Nvidia and AI chip start-up Groq is delivering substantial payouts to employees and investors without a traditional acquisition or equity transfer. Under the non-exclusive licensing deal, most Groq employees are expected to join Nvidia with a mix of cash payouts and stock, while Groq continues operating independently. The structure reflects a growing trend in AI dealmaking designed to secure talent and technology while minimizing antitrust risk, highlighting the enormous financial stakes surrounding AI hardware innovation.

Key Takeaways

• A non-traditional deal structure: Nvidia avoided a full acquisition while still valuing Groq at $20 billion through a licensing agreement.
• Employees and investors benefit significantly: Most Groq shareholders and staff are receiving major cash and stock payouts, often with accelerated vesting.
• Antitrust pressure is shaping AI deals: Big Tech companies are increasingly using creative deal structures to avoid regulatory scrutiny.

(Source: Axios)

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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. 

From Disney to City Hall: New Partnerships, Policies, and Public Impact

When attempting to extrapolate how the present developments in generative AI will lead to the platform of the future, there are a couple of stories to dive deeper into—and what we're looking at this week.

One, which was kind of a small development, was Google's Deep Research. The ability now is that developers are able to use the API to build apps with this capability. If you're not familiar with Deep Research, it's definitely something you should check out. Google was really the first AI offering to provide this functionality in its Gemini model, and it is something quite amazing.

Understanding that the platform of the future is going to be a composite technology—this is really where Deep Research comes in. The LLM will not just respond to prompts but will actually go out and research on the web. It's that combination of using its natural language processing capabilities to actually go do something. This is where we have our first glimpse into what agentic looks like, and it's pretty amazing. 

I demoed Deep Research with other members of the faculty, and they were impressed. It's a good illustration of how this could potentially help tax professionals and accounting professionals do research. Even if you don't trust the output, it does provide a good set of links at the bottom of the page, which enables you to verify. If you're looking at it in-app, there's a way to export Deep Research into a Google Doc. But if you're looking within the actual interface of Gemini, you can go paragraph by paragraph to see what links it's providing and then refine your research from there.

The second story to check out is OpenAI's partnership with Disney. I've felt for a very long time that generative AI will be the ultimate amplifier for storytelling and user-generated content. What it does is give the capability to someone who has great ideas but isn't a professionally trained writer—someone who's not able to get to Hollywood but has great ideas. This is similar to user-generated content, like Dude Perfect—someone who has great ideas but wants to tell a story about Darth Vader. With Disney now owning these kinds of properties—Marvel, Star Wars—it will open up that capability for people with great ideas to create.

Now, I think it's an interesting aspect here, because there's a tendency to think that generative AI is just about cheating. As OpenAI and Disney kind of finalize their partnership, what'll be interesting is to see the tools that are necessary to create the tool of the future. What does that look like when you're trying to create an animation? What does a GenAI video content generator look like? This could be financial salvation for OpenAI. I think the scope is limited right now—it's just social animation, so you're only using Sora within the context of the app.

But regardless, we've seen the success of user-generated content, and it's arguably one of the reasons why Quibi failed during the pandemic—star power doesn't have that much power anymore. That's something of a bygone era. Now, what matters is user-generated content. You can see this with vlogging videos out there that really illustrate the capability of being able to tell a story in a unique way.

There'll be many who argue that this is not real art, that this is not the same as "real" human-generated content—and that's fair. But I would articulate that this is similar to electronic music. People would argue that electronic dance music, or EDM, is not real music. But it created a different genre. It's not like classical music; it's not like rock music. So if you're a fan of CCR—Creedence Clearwater Revival—you're going to argue that techno music is not real music. But it created a different genre and a different type of audience. And I think that either the story is good or bad—that's kind of what it comes down to.

What will enable OpenAI to potentially become its own kind of movie studio is the ability to create a specific filmmaking tool. Most video editors use tools like Premiere Pro or DaVinci Resolve. Most learn these tools through YouTube videos - no certification required.   

And I think that's one of the pathways to the future, because there's been a lot of anti-OpenAI rhetoric out there—comparisons to Myspace and things like that by certain detractors. However, the challenge is to chart the pathway to the future: how do we build something new?

This is where AI builds, not just displaces. The appetite for professionally crafted stories—Star Wars, anime, the next great cinematic experience—isn't going anywhere. But alongside it, we're watching a new genre emerge: stories created by everyday people, powered by tools that didn't exist five years ago. The next Dude Perfect might not just be doing trick shots—they might be producing their own animated series. That's not a threat to storytelling. That's its next chapter.

Disney and OpenAI Strike Landmark Deal to Bring Iconic Characters to Generative AI

The Walt Disney Company and OpenAI announced a three-year licensing and partnership agreement that will allow OpenAI’s generative video platform, Sora, and ChatGPT Images to create fan-inspired short-form videos and images using more than 200 characters from Disney, Pixar, Marvel, and Star Wars. Users will be able to generate short, shareable social videos featuring iconic characters, environments, and props, with curated selections eventually streaming on Disney+. Beyond licensing, Disney will become a major OpenAI customer, integrating OpenAI’s APIs into new products and experiences, including Disney+, and deploying ChatGPT internally. Disney will also make a $1 billion equity investment in OpenAI. Both companies emphasized responsible AI use, including safeguards for creators’ rights and user safety, positioning the agreement as a model for collaboration between AI and entertainment leaders.

(Source: OpenAI)

• Generative fan content expands: Fans will be able to create short AI-generated videos and images using hundreds of Disney-owned characters.
• Strategic partnership deepens: Disney will invest $1 billion in OpenAI and adopt its technology across products and internal operations.
• Responsible AI focus: Both companies stress protections for creators, users, and intellectual property.

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How Saskatoon Is Using AI to Keep City Buses—and Services—Running Smoothly

Saskatoon Transit is using artificial intelligence to improve fleet reliability by identifying mechanical issues before buses break down. Hardware installed on more than 130 buses sends real-time sensor data to a central system, where AI analyzes performance and flags maintenance needs. Since launching as a pilot in 2023, the system has reduced unscheduled maintenance, lowered parts costs, and improved service reliability. AI is also being used across Saskatoon’s water services, waste management, administration, and energy efficiency systems. Nationally, adoption is growing, with many Canadian municipalities using or evaluating AI tools to support operations. While cost, privacy, and data accuracy remain concerns, experts say AI is increasingly seen as a way to modernize services without displacing workers.
(Source: CTV News)

• Predictive maintenance in transit: AI helps Saskatoon detect bus issues early, reducing breakdowns and costs.
• Municipal adoption is rising: Cities across Canada are experimenting with AI in services like HR, infrastructure, and traffic analysis.
• Efficiency without layoffs: AI is being used mainly to automate routine tasks rather than replace workers.

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The Real AI Fear Isn’t a Bubble—it’s Mass Layoffs and Inequality

A commentary in The Guardian argues that public anxiety around artificial intelligence centers less on speculative tech bubbles and more on the risk of widespread job losses and rising income inequality. Citing warnings from AI executives, economists, and policymakers, the piece highlights concerns that AI could eliminate millions of jobs, particularly entry-level white-collar roles. MIT economist and Nobel laureate Daron Acemoglu describes two possible paths for AI: one that maximizes automation and job cuts, and another that enhances workers’ skills and productivity. The article calls for stronger government intervention, including retraining programs, healthcare reform, shorter workweeks, and expanded unemployment insurance, to ensure AI benefits are more evenly distributed.
(Source: The Guardian)

• Job security is the main concern: Many fear AI will lead to mass layoffs and greater inequality.
• Two paths for AI: Experts argue AI can either replace workers or be designed to augment their skills.
• Policy response needed: Governments may need to act to protect workers and modernize safety nets.

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Trump Executive Order Seeks to Block State AI Rules in Favor of National Framework

President Donald Trump signed an executive order aimed at preventing states from enforcing their own artificial intelligence regulations while the federal government works toward a unified national framework. Administration officials say the move is intended to prevent a patchwork of state rules that could slow innovation and weaken US competitiveness. Critics argue the order could undermine consumer protections and accountability, particularly in areas such as deepfakes, discrimination, healthcare, and policing. The decision has exposed divisions within Congress and the Republican Party, and legal experts expect court challenges. Many stakeholders now say Congress faces increased pressure to pass comprehensive federal AI legislation.
(Source: CNN)

• Federal preemption effort: The executive order seeks to limit state-level AI regulation.
• Ongoing debate: Supporters cite innovation and competitiveness, while critics warn of weakened safeguards.
• Legislative pressure grows: Congress may need to establish clear federal AI rules.

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Google and OpenAI Trade Blows as Deep Research and GPT-5.2 Launch Side by Side

Google unveiled a major upgrade to its Gemini Deep Research agent on the same day OpenAI released GPT-5.2, highlighting intensifying competition in advanced AI. Built on Gemini 3 Pro, the new agent allows developers to embed deep research capabilities into their own applications through a new Interactions API. Google says the tool is designed to handle large volumes of information while minimizing hallucinations during complex, multi-step tasks. The company introduced a new open-source benchmark to demonstrate progress, though OpenAI’s near-simultaneous release of GPT-5.2 quickly shifted attention back to the broader AI rivalry.
(Source: TechCrunch)

• More capable research agents: Google’s update enables deeper, more autonomous research workflows.
• Accuracy remains critical: Reducing hallucinations is key for long-running AI tasks.
• Competition is accelerating: Major AI players continue to release upgrades at a rapid pace.

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. 

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.