Showing posts with label EY. Show all posts
Showing posts with label EY. Show all posts

Thursday, January 15, 2026

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. 

Friday, December 30, 2016

RPA and the Accountant: A path out of the mundane?

One of the latest hype technologies is Robot Process Automation (RPA).

My first question when coming across this, is what is the difference between this and cognitive computing? 

As can be seen by these videos, it's more about "dumb" automation instead of "smart" innovation: where routine tasks are handled by the system instead of a person. This is in contrast to something like IBM's Watson, which attempts to understand language and offer probabilistic judgments as to what is the right answer to a question like it did on Jeopardy!


The first video (produced by Deloitte UK) does a great job of actually showing us how RPA can automate the process of extracting information/documents from email and the generating invoices through the company's ERP:



The strength of this video (produced by EY) is showing us the business case for RPA:


The idea is that RPA can automate routine tasks, instead of offshoring. In other words, it brings the world of automation onced reserved for the assembly line to the back office.

As described in this Deloitte publication, it puts RPA as the first step towards a cognitive enterprise - automate the task and then bring cognitive, AI, machine learning, etc., into the process to make it smarter.

To use a maturity model approach, RPA is the first level in bringing together the necessary data and processes to actually train the algorithm to make it smarter.

What does this mean for auditors and accountants?

For accountants, the back office is going to require less people in terms of executing these mundane tasks.

However, this doesn't necessarily mean that jobs will be lost.

As with the advent of cloud computing, the enterprises will have to determine whether such talent can be used more effectively to improve the quality of financial reporting and work on the back log of finance projects that haven't been attended due to staff working on these low-value tasks. That being said, the problem of meeting quarterly targets to feed investors insatiable desire for profits is something that can't be ignored when discussing whether management will choose profits over better processes.

For auditors the story is a little different.

The reality of the profession is that it can't retain talent because people find the work unsustainable: it's hard to shutdown your personal life for a third of the year or more to meet the needs of clients during busy season.

RPA and automation could make the profession more sustainable, as these mundane tasks could be handed to a system instead of a junior. This is similar to the "race with the machine" concept I mentioned in this post, when referring how Watson is helping doctors treat cancer.  Auditor could then focus on more value added tasks, such as assessing aggregate risks, industry trends, etc. Such insights will improve audit quality and give clients better understanding of business and audit risks, making the work more interesting for both auditors and auditees alike.

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else.