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. 

UWCISA's 5 Tech Takeaways: Jobs, Power, Platforms, and the Rise of AI Agents

An interesting piece in Inc. (below), makes the case that experience is the real advantage in the age of AI. Joel Comm argues that unlike previous tech waves that rewarded coding ability, AI rewards the ability to ask the right questions and interpret results strategically. 

I make this point often in my prompting sessions: the better you know your domain, the better you prompt. A privacy specialist who understands "notice, choice, and consent" will get fundamentally different results from an LLM than someone who just types "tell me about privacy." The same applies across every field. An auditor who knows what a control deficiency looks like, a tax professional who understands transfer pricing rules, or a cybersecurity analyst who understands the ISO 27001 info-sec framework, will all extract sharper, more actionable outputs from AI. The tool does not know what matters. You do. That is the gap that no amount of prompt engineering tricks can close. AI rewards expertise. It does not replace it.

Prompt: "Aerial photorealistic view of a wide river splitting into multiple smaller streams flowing through a green valley, lush vegetation, dramatic cloud formations, vibrant natural colors, drone perspective, ultra-realistic detail"

The 6 Jobs Least Likely to Be Replaced by AI

A report from AI company Anthropic highlights that many jobs requiring physical, hands-on work and in-person interaction face the lowest risk of being replaced by artificial intelligence. According to the report, about 30% of jobs have minimal exposure to AI automation, particularly roles that involve real-world tasks that machines struggle to perform reliably. Examples include cooks, motorcycle mechanics, lifeguards, bartenders, dishwashers, and dressing room attendants. The broader trend suggests that industries such as skilled trades, hospitality, agriculture, maintenance, and personal services are relatively safer from AI disruption. Meanwhile, jobs heavily dependent on data, software, and digital workflows—including programmers, customer service representatives, and financial analysts—face greater exposure. Despite the risks, the report notes that AI is currently boosting productivity rather than causing mass unemployment, although early signals such as slower hiring among young workers in high-exposure fields suggest the labor market may gradually shift as AI capabilities improve.

(Source: Forbes)

Key Takeaways

• Hands-on work remains resilient: Jobs involving physical tasks and in-person service are far less vulnerable to AI automation.
• Digital jobs face higher exposure: Roles centered on data, coding, or analysis are more likely to be reshaped by AI tools.
• AI is augmenting more than replacing—for now: While productivity is increasing, there is not yet widespread unemployment directly caused by AI.

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Why Being Over 50 Could Be a Superpower in the AI Era

In the age of artificial intelligence, experience may matter more than technical skill. Joel Comm argues that while younger founders may move quickly building AI tools, seasoned professionals often have a key advantage: judgment built from decades of experience. Unlike previous tech waves that rewarded coding ability, AI increasingly rewards the ability to ask the right questions and interpret results strategically. Experienced leaders can use AI to pressure-test ideas, identify blind spots, and refine strategies instead of blindly accepting outputs. Comm also warns that organizations risk making poor decisions if they treat AI as a strategy generator rather than a thinking partner. As AI tools become more accessible, pattern recognition, business judgment, and strategic thinking may become the true competitive advantages in the AI era.

(Source: Inc.)

Key Takeaways

• Experience is a strategic asset: Pattern recognition built over decades can make experienced professionals highly effective with AI tools.
• AI rewards better questions: Strategic thinking may matter more than technical ability when working with AI.
• Human judgment remains essential: Leaders who rely entirely on AI risk outsourcing critical decision-making.

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Anthropic Bets on an AI App Ecosystem with Claude Marketplace

Anthropic has launched Claude Marketplace, a new platform allowing enterprises to access specialized tools powered by Claude through third-party partners such as GitLab, Replit, Snowflake, and Harvey. Companies with existing Anthropic contracts can allocate part of their spending commitments toward these partner applications, simplifying procurement and billing. Rather than replacing traditional enterprise software, the marketplace emphasizes collaboration between Claude’s reasoning capabilities and specialized applications that add domain expertise, integrations, and compliance features. The initiative also reflects a broader trend in AI platforms toward ecosystems of apps and integrations. However, Anthropic’s biggest challenge will be convincing enterprises to adopt these marketplace tools instead of building their own custom AI workflows.

(Source: VentureBeat)

Key Takeaways

• Centralized AI marketplace: Businesses can access partner-built AI tools using existing Anthropic commitments.
• AI plus domain expertise: Partner apps provide industry-specific workflows that standalone AI models cannot easily replicate.
• Enterprise adoption is key: Success depends on whether companies integrate these marketplace tools into daily workflows.

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GPT-5.4 Introduces More Powerful AI Agents to ChatGPT

OpenAI has launched GPT-5.4, a new AI model designed to enhance professional workflows and expand agent-based capabilities. The model integrates improvements in reasoning, coding, and autonomous task execution into one system. A major upgrade is native computer-use capability, enabling the model to interact directly with operating systems, issue keyboard and mouse commands, and execute tasks across applications on behalf of users. OpenAI says GPT-5.4 also delivers improved accuracy, with responses reportedly 33% less likely to contain errors compared to GPT-5.2. The release arrives as OpenAI seeks to regain momentum following controversy around its partnership with the U.S. Department of Defense, which triggered backlash from some users and employees.

(Source: Gizmodo)

Key Takeaways

• AI agents get more powerful: GPT-5.4 can operate computers directly and complete tasks autonomously.
• Fewer errors: OpenAI says the model produces fewer mistakes and hallucinations than earlier versions.
• Strategic timing: The release aims to rebuild momentum for ChatGPT following recent controversy.

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Alberta’s Plan to Power the AI Boom with Self-Sustaining Data Centres

Alberta is positioning itself as a major destination for AI infrastructure by encouraging companies building data centres to generate their own electricity rather than relying solely on the provincial grid. The province hopes to attract more than $100 billion in AI data centre investment over five years, citing advantages such as abundant land, cold climate conditions, and a deregulated electricity market. The policy requires developers to bring their own power generation and pay for grid upgrades needed to support their operations. This approach contrasts with some U.S. regions where data centre expansion has strained power grids and increased energy costs for residents. By requiring companies to handle their own energy needs, Alberta aims to support rapid AI infrastructure growth while protecting grid stability and consumer electricity prices.

(Source: CBC News)

Key Takeaways

• Self-powered infrastructure: Alberta encourages data centres to generate their own electricity for AI operations.
• Major investment opportunity: The province aims to attract over $100 billion in AI infrastructure investment.
• Protecting the grid: The policy helps prevent energy price increases and reliability issues for residents.

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. 

Could 2026 Be Anthropic's Year? $30 Billion in Funding, a Spicy Super Bowl Ad, and a Trillion-Dollar Wake-Up Call

Could 2026 Be Anthropic's Year? 

 The year started with a bang for the maker of Claude.ai. As we covered previously, CEO Dario Amodei was featured in a debate with Demis Hassabis, and it has been quite the ride since. If you missed the company's spicy Super Bowl ad taking a shot at OpenAI's decision to bring ads to ChatGPT, check it out below. 

This post focuses on Anthropic and Claude. I have to confess: I have been a Claude fan for a long time. I found the writing quality noticeably better, especially during my prompting sessions for CPA Ontario, UWCISA, and others. To be fair, OpenAI closed the gap significantly when they introduced Canvas. I will be running a course in about a week comparing the major LLMs (see link here). 

 So, yes, I am arguably biased. But the numbers speak for themselves. Anthropic raised $30 billion this year at a $380 billion valuation, on top of $13 billion last year. The company reports $14 billion in run-rate revenue, growing over 10x annually for three consecutive years. Claude Code alone has hit a $2.5 billion run rate. They are reportedly on track to be profitable, and IPO rumors continue to circulate. Whether or not they go public this year, the trajectory is hard to ignore. 

Over the next few weeks, we will be exploring Anthropic's expanding toolset, including the recently released Cowork for Windows. There is also some controversy worth examining. But the broader picture is clear: Anthropic is not just competing in enterprise AI. It is reshaping the conversation about what these tools can do. I convinced a good friend that Anthropic is the way to go, and he finally came on board.

Claude’s Upgrade Sparks Trillion-Dollar Market Rout

Anthropic’s release of industry-specific plug-ins for its Claude Cowork tool and the debut of Claude Opus 4.6 triggered a sweeping selloff across enterprise software stocks, as investors feared AI could disrupt traditional SaaS business models. Opus 4.6 introduces a powerful new capability: coordinated teams of autonomous AI agents that can divide and execute complex professional tasks in parallel — from financial research and due diligence to presentation building via a direct PowerPoint plug-in. The model’s expanded 1-million-token context window allows it to process massive datasets at once, strengthening its usefulness in financial and knowledge-intensive work. Financial data firms like FactSet, S&P Global, Moody’s, and Nasdaq saw notable declines amid concerns that AI could automate high-margin research functions. While some analysts argue fears of a “SaaSapocalypse” are premature, Anthropic’s expansion beyond coding into broader enterprise workflows signals mounting competitive pressure across the software industry. (Source: Yahoo Finance)

• Enterprise shockwaves: New Claude upgrades sparked sharp declines in financial data and enterprise software stocks.
• Agent team breakthrough: Opus 4.6 enables coordinated AI agents to handle complex, multi-step professional projects.
• Automation acceleration: Expanded context processing and financial analysis capabilities increase competitive pressure on traditional SaaS models.

Anthropic Scores Big: Super Bowl Ad Delivers 11% User Surge

Anthropic saw a measurable surge in user activity following its Super Bowl ad that criticized OpenAI’s move to introduce ads into ChatGPT, according to BNP Paribas data. Website visits to Anthropic’s Claude chatbot rose 6.5% after the game, and daily active users increased 11% — the largest jump among major AI competitors featured during the broadcast. Claude also broke into the top 10 free apps on Apple’s App Store. In comparison, OpenAI’s ChatGPT saw a 2.7% boost in daily active users, while Google Gemini gained 1.4%. The high-profile ad battle underscores the intensifying rivalry between Anthropic and OpenAI, both of which are racing toward potential IPOs and competing fiercely for enterprise clients, top talent, and record-breaking funding rounds. (Source: CNBC)

• Super Bowl impact: Anthropic experienced an 11% increase in daily active users and a 6.5% rise in site visits following its ad criticizing OpenAI.
• AI ad showdown: Anthropic, OpenAI, Google Gemini, and Meta all used Super Bowl ads to compete for market share in the rapidly growing AI sector.
• Escalating rivalry: With potential IPOs on the horizon and massive funding rounds underway, competition between Anthropic and OpenAI is becoming increasingly public and aggressive.

Anthropic Lands $30 Billion to Cement Enterprise AI Dominance

Anthropic has raised $30 billion in Series G funding at a $380 billion post-money valuation, solidifying its position as a dominant force in enterprise AI and agentic coding. The round was led by GIC and Coatue, with participation from a wide range of major institutional investors, including BlackRock, Sequoia Capital, Goldman Sachs, Microsoft, and NVIDIA. The company reports a $14 billion revenue run rate, growing more than 10x annually for three consecutive years. Enterprise adoption has surged, with over 500 customers now spending more than $1 million annually and eight of the Fortune 10 companies using Claude. Claude Code, launched publicly in 2025, has reached a $2.5 billion run-rate revenue and now accounts for an estimated 4% of GitHub public commits worldwide. Anthropic says the new funding will support frontier research, product development, and infrastructure expansion across AWS, Google Cloud, and Microsoft Azure. (Source: Anthropic)

• Massive capital raise: Anthropic secured $30 billion in Series G funding at a $380 billion valuation, with backing from top global investors.
• Explosive enterprise growth: The company reports a $14 billion revenue run rate, 10x annual growth, and over 500 customers spending more than $1 million per year.
• Claude Code momentum: Claude Code now generates $2.5 billion in run-rate revenue and is responsible for an estimated 4% of public GitHub commits worldwide.

AI Safety Leader Quits Anthropic, Warning the ‘World Is in Peril’

A senior AI safety researcher, Mrinank Sharma, has resigned from Anthropic, warning in a public letter that the “world is in peril” due to interconnected crises including artificial intelligence and bioweapons. Sharma, who led research into AI safeguards such as preventing AI-enabled bioterrorism and examining how AI systems influence human behavior, said he struggled with the pressures companies face to compromise their values. He announced plans to return to the UK to study poetry and write, stepping away from the AI industry. His departure follows another high-profile resignation at OpenAI, where researcher Zoe Hitzig cited concerns about the psychological and societal impact of introducing advertising into ChatGPT. The resignations highlight growing internal tensions within leading AI firms as they balance rapid commercialization with safety and ethical considerations. (Source: BBC)

• Safety concerns intensify: Anthropic’s AI safety lead resigned, warning of global risks tied to AI, bioweapons, and broader systemic crises.
• Industry unease: A separate OpenAI researcher also stepped down over concerns about ads and the psychosocial impact of AI tools.
• Commercialization vs. values: The departures underscore mounting tension between rapid AI growth, monetization strategies, and ethical safeguards.

How Claude Helped Slash a $195,000 Hospital Bill by $163,000

Marketing consultant Matt Rosenberg used Anthropic’s AI assistant Claude to help negotiate a $195,628 hospital bill down to approximately $32,500 after his brother-in-law died following a heart attack. By prompting Claude to analyze billing codes and compare them to Medicare reimbursement rules, Rosenberg uncovered improper “unbundling” of procedures and questionable charges that Medicare would not have allowed. Claude estimated Medicare would have paid roughly $28,675 for the same services. Rosenberg verified the findings using ChatGPT and independent research before sending a detailed letter to the hospital outlining the discrepancies. Within a week, the hospital agreed to a dramatically reduced settlement. Rosenberg argues that AI tools are shifting the power balance in complex systems like healthcare billing by making opaque regulations more accessible to patients. (Source: Business Insider)

• AI as negotiation tool: Claude helped identify billing irregularities and Medicare bundling rules, enabling a $163,000 reduction in charges.
• Verification matters: The author cross-checked Claude’s findings with ChatGPT and direct Medicare documentation to avoid AI “hallucinations.”
• Shifting power dynamics: AI tools can help patients navigate complex healthcare systems that often disadvantage the uninsured.

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 @ Davos: Google and Anthropic CEOs Admit What's Already Happening to Jobs

Each year, the world’s most influential figures convene at the World Economic Forum in Davos. This event serves as a premier platform where leaders from business, government, and academia come together to discuss and address pressing global issues. Although the Prime Minister’s speech was top of mind, considerable attention was also directed toward the topic of AI.

The discussion that caught my attention was when two of the most influential figures in AI sat down for a rare joint appearance. Dario Amodei, CEO of Anthropic, and Demis Hassabis, CEO of Google DeepMind, discussed what they called "The Day After AGI" with The Economist's Zanny Minton Beddoes moderating. The conversation covered familiar ground on timelines and risks, but several business-relevant admissions stood out.

During the discussion, Demis Hassabis of Google DeepMind and Dario Amodei of Anthropic laid out a series of profound technological, economic, and geopolitical shifts they believe are set to unfold within the next five years. Five disclosures from the discussion deserve closer attention.

Anthropic's revenue trajectory is tied directly to model capability.

Amodei stated that Anthropic's revenue grew from zero to $100 million in 2023, to $1 billion in 2024, to $10 billion in 2025. That is 100x growth in three years. But the more telling point was how he framed it: "There's been a kind of exponential relationship not only between how much compute you put into the model and how cognitively capable it is, but between how cognitively capable it is and how much revenue it's able to generate." The implication is that revenue follows capability in a non-linear way. Each step improvement in the model produces disproportionately larger commercial returns. Bloomberg reported that Anthropic's revenue run rate had topped $9 billion by the end of 2025, corroborating Amodei's claims.

Google is already seeing hiring impacts at the junior level.

Hassabis was direct: "I think we're going to see this year the beginnings of maybe impacting the junior level entry-level jobs, internships, this type of thing, and I think there is some evidence. I can feel that ourselves, maybe like a slowdown in hiring." This is not speculation about future displacement. The CEO of Google DeepMind is describing what is happening inside Google now. When Amodei was asked about the same topic, he did not back away from his previous prediction that half of entry-level white-collar jobs could disappear within one to five years. He added that he can "look forward to a time where on the more junior end and then on the more intermediate end we actually need less and not more people" at Anthropic itself.

Amodei compared chip sales to selling nuclear weapons.

When the moderator raised the current administration's approach to selling chips to China, Amodei's response was as follows: "I think of this more as like, you know, it's a decision—are we going to sell nuclear weapons to North Korea and you know because that produces some profit for Boeing... I just don't think it makes sense." He argued that restricting chip sales would shift the competition from a US-China race to a Google-Anthropic race, which he said he is "very confident we can work out."

Some engineers at Anthropic no longer write code.

Amodei revealed that "I have engineers within Anthropic who say I don't write any code anymore. I just let the model write the code. I edit it. I do the things around it." He estimated they might be six to twelve months away from models doing "most, maybe all" of what software engineers do end-to-end. This is not a prediction about industry-wide adoption. It is a description of current practice at one of the leading AI companies.

Research-led companies may have an advantage.

Both executives made the same observation from different angles. Amodei noted that "companies that are led by researchers who focus on the models, who focus on solving important problems in the world, who have these hard scientific problems as a North Star" are the ones likely to succeed. Hassabis described Google DeepMind as "the engine room of Google" and emphasized that getting "the intensity and focus and the kind of startup mentality back to the whole organization" had been essential. The subtext: companies that treat AI as an IT function rather than a research priority may find themselves at a structural disadvantage.

Closing thoughts

What I thought was distinctive about the discussion is that both CEOs recognized the importance of research. Though there is a lot more to be said about this, arguably it is the ability of AI to tackle R&D that could enable scientific breakthroughs where this was previously not feasible. Amodei has written extensively on this point. In his essay Machines of Loving Grace, he argued that AI-enabled biology and medicine could compress the progress that human biologists would have achieved over the next 50-100 years into 5-10 years. We will be looking at this topic in future posts.

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: Next-Gen AI at CES 2026, Changing Job Dynamics, and High-Stakes Tech Rivalries

This edition looks at AI and digital infrastructure from five angles: NVIDIA’s latest hardware platform, Anthropic’s deep dive on how AI is actually used in the economy, frontline “AI fatigue” inside EY Canada, record-breaking frustration with Canadian telecoms, and Nvidia’s chips turning into bargaining tools in U.S.–China geopolitics. Together, they sketch a picture of powerful technology racing ahead while workers, consumers, and governments scramble to keep pace.

NVIDIA’s Rubin Platform Sets the Stage for Gigascale AI at CES 2026

NVIDIA CEO Jensen Huang opened CES 2026 by unveiling Rubin, the company’s first extreme-codesigned, six-chip AI platform, designed to dramatically cut the cost of AI training and inference while accelerating model development. As the successor to Blackwell, Rubin tightly integrates GPUs, CPUs, networking, storage and software to reduce bottlenecks and deliver AI tokens at roughly one-tenth the previous cost. Alongside Rubin, NVIDIA expanded its open-model ecosystem across healthcare, climate science, robotics, embodied intelligence, and autonomous driving, including Alpamayo, a reasoning VLA model family aimed at Level-4 autonomy and showcased in the new Mercedes-Benz CLA. Huang also highlighted the rise of “physical AI” through robotics, simulation, and industrial partnerships with companies like Siemens, while rolling out consumer-facing news such as DLSS 4.5, RTX gaming updates, and new GeForce NOW options—all reinforcing NVIDIA’s ambition to provide a full-stack AI infrastructure from data center to desktop to car.

• Rubin slashes AI costs: Rubin promises roughly 10x cheaper token generation by co-designing GPUs, CPUs, networking, storage, and software into a single extreme-scale AI platform.
• Open models across six domains: NVIDIA’s open models now span healthcare, climate, reasoning, robotics, embodied intelligence, and autonomous driving, giving developers a broad foundation for new AI applications.
• Physical and personal AI converge: From Level-4-capable vehicles to desktop “personal agents” and RTX gaming tech, NVIDIA is pushing AI into cars, robots, and consumer devices—not just supercomputers.

(Source: NVIDIA Blog)

Inside Claude’s Global Impact: New Data Shows Productivity Gains and Shifting Job Skills

The January 2026 Anthropic Economic Index introduces “economic primitives,” a set of new metrics that describe how people and firms actually use Claude: task complexity, human and AI skill levels, autonomy, use cases, and task success. Drawing on one million anonymized conversations and API calls from late 2025, the report finds that Claude is disproportionately used for high-skill, high-education tasks and tends to deliver larger time savings on more complex work—though reliability drops as tasks become longer and harder. Adoption patterns differ sharply by geography: higher-income, higher-education regions use Claude more collaboratively and for personal or diversified work, while lower-income countries lean more on coursework and targeted technical tasks. When success rates are factored in, the report suggests AI could still add about one percentage point to annual labour-productivity growth over the next decade, but also warns that automation tends to remove the most education-intensive tasks within many jobs, potentially “deskilling” roles even as it boosts efficiency.

• New “economic primitives” map real AI use: Anthropic introduces foundational metrics to quantify how Claude is used—covering complexity, skills, autonomy, use case, and task success across millions of interactions.
• Biggest gains on complex tasks, but with reliability tradeoffs: Claude speeds up higher-skill work the most, yet success rates fall as tasks get longer or more complex, meaning realistic productivity estimates must discount for failures.
• AI reshapes job content and inequality: Usage concentrates on higher-education tasks, often automating the most skill-intensive parts of jobs and potentially deskilling roles, while regions with more education and income are better positioned to benefit.

(Source: Anthropic)

EY Canada Confronts Rising ‘AI Fatigue’ as Workers Feel Overwhelmed by Rapid Change

EY Canada has invested heavily in AI training—400,000 hours of learning time and a $12 million internal program since 2022—but is now grappling with “AI fatigue” among parts of its workforce. After segmenting employees by both skill and willingness to use AI, the firm found that some professionals felt so overwhelmed by the pace of change they didn’t know where to start. In response, EY is tailoring its approach with bespoke learning paths, more guidance on ethical and responsible AI use, and sandbox environments where skeptical staff can experiment without risk. This reflects a wider pattern: across consulting, law, and other white-collar sectors, workers report burnout as AI tools, training requirements, and vendor pitches stack on top of already long workweeks. While some firms are tying promotions and hiring to AI proficiency, EY emphasizes human-in-the-loop oversight—especially for more fragile agentic AI systems—and insists it still plans to hire junior talent rather than replacing entry-level roles outright.

• AI fatigue is a real adoption barrier: Even after large-scale training, some EY staff feel overloaded and disengaged, forcing the firm to rethink how it introduces AI into everyday workflows.
• Personalized, empathetic training is emerging as critical: EY is segmenting employees by “skill” and “will,” using bespoke learning, ethical guidance, and safe sandboxes to engage skeptics instead of simply pushing more generic courses.
• Human oversight remains central, despite automation pressure: The firm stresses that fragile tools like agentic AI still require trained humans in the loop, and continues to recruit entry-level consultants rather than fully automating junior work.

(Source: The Logic)

Telus Sees 78% Complaint Surge as Billing and Contract Issues Rise Nationwide

Canada’s telecom watchdog, the Commission for Complaints for Telecom-television Services (CCTS), reports that consumer complaints have hit a record high, rising 17% to 23,647 accepted cases over the past year. Wireless services remain the biggest source of frustration, but billing problems—incorrect charges and missing credits—make up nearly 46% of all issues. Among the “Big 3” carriers, Rogers leads with 27% of total complaints, while Telus accounts for 21% but suffers the sharpest increase: a 78% year-over-year jump in complaint volume. Bell sits at 17% of the total. The report also flags a 121% spike in breach-of-contract complaints, including fee hikes and broken promises on features, alongside persistent service issues such as outages and installation delays. Although many Canadians still don’t know the CCTS exists, it remains a free avenue for unresolved disputes—and says it successfully resolves most cases. Still, with TV-related complaints up 44% and billing errors at a five-year high, the data paints a grim picture for customer experience in Canada’s concentrated telecom market.

• Record complaint levels across Canadian telecoms: The CCTS logged 23,647 accepted complaints—a 17% jump—driven heavily by wireless issues and billing disputes.
• Telus stands out for rapid deterioration: While Rogers still generates the most complaints overall, Telus suffered a 78% increase in cases, far outpacing Bell and indicating a sharp drop in customer satisfaction.
• Broken contracts and billing errors dominate frustration: Breach-of-contract complaints surged 121%, while billing problems hit a five-year high, underscoring systemic issues in pricing transparency and service reliability.

(Source: iPhone in Canada)

Nvidia’s H200 Becomes Geopolitical Leverage as China Restricts Purchases

China has instructed customs agents that Nvidia’s H200 AI chips are “not permitted” to enter the country and advised domestic tech firms to avoid buying them unless absolutely necessary, creating what sources describe as a de facto—if not yet formal—ban. The directive comes just as the U.S. government approved exports of the H200 to China under certain conditions, turning the chip into a focal point of U.S.–China tech tensions ahead of President Donald Trump’s planned April visit to Beijing. Analysts suggest Beijing may be using the restrictions as bargaining leverage or to push demand toward domestic AI processors like Huawei’s Ascend 910C, which still lag Nvidia’s performance for large-scale model training. The stakes are enormous: Chinese companies have reportedly ordered more than two million H200 units at around US$27,000 each, far exceeding Nvidia’s inventory, while the U.S. stands to collect a 25% fee on chip sales. Whether these moves ultimately favor China’s chip ambitions or Nvidia’s bottom line remains unclear, but the H200 has clearly become a strategic asset in a broader struggle over AI hardware dominance.

• China imposes a de facto block on H200 chips: Customs guidance and warnings to tech firms effectively halt Nvidia H200 imports for now, even though it’s unclear if this is a formal or temporary measure.
• Chips become negotiation tools in U.S.–China relations: The timing—just after U.S. export approval and ahead of high-level talks—suggests Beijing may be using access to H200s as leverage in broader tech and trade negotiations.
• Huge commercial and strategic stakes on both sides: Chinese firms have ordered millions of H200s, while the U.S. benefits from export fees and strategic influence, making the chip central to the evolving AI power balance.

(Source: Reuters)

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.