SACRAMENTO — National union leaders, including the head of one of California’s largest labor organizations, on Wednesday urged Gov. Gavin Newsom to protect workers as artificial intelligence threatens to replace or surveil employees — and warned that a failure to do so could hurt his presidential ambitions.
“This is a priority for the entire nation,” Lorena Gonzalez, president of the California Federation of Labor Unions, said at a news conference near the state Capitol. “He cannot spend his time waiting to be done in California and think he’s not going to get questions about the true issues surrounding AI, Big Tech and the Big Tech billionaires that are trying to buy our government.”
Gonzalez, a former state lawmaker from San Diego, said the federation is sponsoring a package of new bills aimed at reining in the use of AI and protecting the rights of workers, including safeguards against spying in the workplace and restrictions on layoffs.
The package of bills supported by labor organizations includes:
Senate Bill 947 by Sen. Jerry McNerney (D-Stockton), which would require human oversight if an algorithm is used to justify the discipline or termination of an employee.
Senate Bill 951, introduced by Sen. Eloise Gomez Reyes (D-Colton), which would require employers to provide a 90-day advance notice to workers and local and state governments before AI-related layoffs. It would apply to cases affecting 25 or more workers or 25% of the workforce, whichever is less. Recent layoffs, including at Amazon, Expedia and Pinterest, have been tied to AI, although some economists argue it’s challenging to determine whether that was the primary factor.
Assembly Bill 1331, dubbed “No bosses in the bathroom,” would grant workers the right to remove workplace surveillance tools when entering public bathrooms or certain employee-only areas. The bill, authored by Assemblymember Sade Elhawary (D-Los Angeles), would subject employers to a $500 civil penalty for violations.
Gonzalez said labor organizations are often told to “work it out” with businesses but argued this was a dead end.
“We are not going to be able to achieve guardrails by working with bosses who want no guardrails,” she said. “It is time that the governor engages with workers in the workplace. Every AI convening he does, everybody he’s pulled together is [representing] AI and Big Tech lobbyists.”
Gonzalez was joined Wednesday by Liz Shuler, president of the AFL-CIO, and other labor leaders from Iowa, Georgia, North Carolina and Nevada.
“This is the most urgent issue that we [as workers] are facing,” Shuler said. “This is a crisis and no one is prepared.”
In a joint letter addressed to Newsom, they implored the governor to act quickly to establish meaningful safeguards around the technology.
“This fight extends beyond devastating job losses and new forms of union busting,” a copy of the letter states. “There is dignity in human work that is the foundation of a healthy, productive democracy. The future of our economy and our society cannot be left to the unchecked whims of profit driven technology corporations and billionaires.”
In an email to The Times, Newsom spokesperson Tara Gallegos said the governor had a strong record of fighting for workers’ rights, including raising the minimum wage and expanding sick leave and other worker protections.
“No Governor has done more than Governor Gavin Newsom to regulate AI in a way that protects workers without killing jobs or innovation,” she wrote. “Under his leadership, California has taken the most comprehensive, worker-centered approach to AI in the country.”
Adults in the United States are growing increasingly concerned about the ramifications of AI, according to a survey from the Pew Research Center. Fifty percent of those surveyed last year said they are “more concerned than excited” about the increased use of AI in daily life, up from 37% in 2021.
Last year, Spotify paid out more than $11 billion to the music industry, bringing the company’s total payouts since launch to nearly $70 billion.
The milestone year reflected the “largest annual payment to music from any retailer in history,” the company announced on Wednesday in a post. In 2025, Spotify’s payout amount grew by over 10%, making the Sweden-based streamer one of the industry’s main revenue drivers.
“Big, industry-wide numbers can feel abstract, but that growth is showing up in tangible ways,” wrote Charlie Hellman, the company’s new head of music. “Despite rampant misinformation about how streaming is working today, the reality is that this is an era full of more success stories and promise than at any point in history.”
When music streaming was first introduced, there was some controversy about how much artists earn from streams. According to Spotify, independent artists and labels accounted for half of all royalties. Additionally, the company said there are currently more artists earning over $100,000 a year from Spotify alone than were getting stocked on shelves at the height of the compact disc era.
Founded in 2006, the company, with a large presence in L.A.’s Arts District, has become the world’s most popular audio streaming subscription service. The platform offers access to over 100 million tracks, podcasts and audiobooks in over 180 markets.
At the top of the year, founder Daniel Ek moved from his CEO position to become executive chairman. Spotify named two co-CEOs, Gustav Söderström and Alex Norström, in his place.
This month, Spotify raised prices for its premium subscribers in the U.S., bringing the costto $12.99 per month. Hellman disclosed that as Spotify’s audience continues to grow, the higher prices are designed to help with the company’s ongoing expansion. According to the post, Spotify makes up roughly 30% of recorded music revenue and pays out two-thirds of all music revenue to the industry. The other third gets invested back into the company to maintain an “unrivaled listening experience.”
Separately, Spotify said it is hoping to counter new developments in AI by reinforcing a human connection between artists and fans. This includes an emphasis on more artist-powered videos, continuing to promote artists’ live shows on the platform and expanding the role of the company’s music curators. The streamer also has plans to crack down on AI-driven artists on the platform.
“AI is being exploited by bad actors to flood streaming services with low-quality slop to game the system and attempt to divert royalties away from authentic artists,” said Hellman. “We’re going to introduce changes to the systems for artist verification, song credits, and protecting artist identity. It’s critical to ensuring listeners and rightsholders can trust who made the music they’re hearing.”
A new wave of agentic AI systems is reshaping banking operations. Unlike typical large language model (LLM) applications that answer prompts, agentic systems execute sequences of actions: querying systems, retrieving documents, transforming data, and producing outputs. Quietly, these autonomous tools are beginning to redefine the banking technology landscape.
The potential impact is sufficiently profound that McKinsey is now framing agentic AI as a structural shift in banking rather than a side bet; the consultant estimates that AI adoption—including agentic AI systems—could reduce banks’ aggregate cost base by 15% to 20%. Bain, in its 2025 report, “State of the Art of Agentic AI Transformation Technology Report,” cites that in the first half of 2025, “tech-forward enterprises” turned their focus from automating tasks to redesigning entire workflows, as early adopters get to grips with how agents—or the AI systems that independently handle multi-step tasks by coordinating tools, data and actions to meet specified objectives—may coexist safely and collaborate productively. Yet progress is limited.
Although agentic AI may hold promise, definitional confusion and implementation hurdles mean very few true use cases exist, cautions Armand Angeli, AI and automation specialist and vice president, Digital Transformation and AI Group, at DFCG, the French network of CFOs.
“Financial institutions still struggle to understand and implement agentic AI properly,” he says, “and are jumping too fast into these new tools without addressing the fundamentals of data quality, clear processes, skillsets, and ROI [return-on-investment]. There’s a high degree of confusion about what agentic AI is, with people equating AI assistants or RPA [robotic process automation] with true agents. Only a very small number are actually building and scaling agentic effectively.”
Angeli also contends that people overuse the word “agentic.”
“GenAI is mistaken for agentic because it seems intelligent or retrieves data,” he says. “But GenAI is relatively simple and doesn’t self-correct, unlike agents with memory and feedback loops for auto-healing and learning. Building these agents requires mapping complex processes and understanding where the data is, which can take months and thousands of euros in costs. It’s a fine line between a simple agent or RPA and true agentic AI.”
Even though the tools themselves are complex, their appeal is straightforward and powerful.
Where Agentic AI Is Actually Being Deployed
Whether LLM-powered information retrieval agents, single-task agentic workflows, cross-system agentic workflow orchestration, or multi-agent constellations, true agentic AI can perform complex tasks independently within defined boundaries, all with limited human intervention.
BBVA Peru’s Blue Buddy agentic AI assistant is an example. The “lightning-fast knowledge synthesizer” autonomously navigates the commercial bank’s vast ecosystem of unstructured data—product manuals, regulations, and complex processes—to deliver precise, contextualized answers in real time and in a risk managed way.
“We’re not just exploring AI; we’re putting it to work on the front lines of our business,” says Benjamín Chávez, head of engineering at BBVA Peru.
UK-based consultant Capco recently deployed an agentic AI assistant at a global investment bank to support junior bankers in producing credit memos, company profiles, and peer benchmarks.
“Previously, analysts could spend five to ten hours a week on a single memo, largely on manual data gathering, formatting, and rewriting,” says Charlotte Byrne, Capco’s UK GenAI lead. “The new workflow allows a banker to request, for example, ‘Draft a credit memo for a corporate client with the latest financials and peers.’ The agent delivers a first draft within minutes.”
The client bank ultimately saw a 50% reduction “in time spent on the mechanical parts of the process.”
Wells Fargo recently announced a collaboration with Google Cloud that will deploy agentic AI at scale via 2,000 employees, with further plans for bank-wide rollout. The tools Google Cloud will supply synthesize information, automate workflows, and boost agility; key applications include triaging foreign exchange post-trade inquiries and navigating guidelines in corporate and investment banking. In Greece, Eurobank is working with EY to develop a scalable, automated system that embeds agentic AI into core banking operations.
In each case, the goal is to replace high-volume, repetitive workflows. But implementation is not without its challenges.
During Capco’s recent rollout, while AI algorithms themselves did not present an issue, the client bank’s internal requirements complicated the process. “We had to use guard-railed, bank-approved models,” says Byrne, “which meant investing heavily in prompt design, retrieval quality, and validation. Governance also added long lead times; simply getting proof-of-concept approvals took nearly two months, by which point the model landscape had already shifted again.”
Engagement was another challenge. Asking already stretched teams to dedicate extra hours to testing is often one of the practical challenges of implementing agentic AI, and adoption suffers if solutions are built too far from the day-to-day workflow. And while banks see the potential of autonomous agents, Byrne observes, few currently have the infrastructure to use them effectively and safely, with poor data and legacy systems the key obstacles.
“Most AI failures in banking have nothing to do with the models themselves,” she says; many banks still lack clean APIs into core systems or struggle with slow, fragmented approval cycles that are incompatible with iterative AI development.
Scaling The Challenge
Scaling GenAI from “lab to regulated banking environment” is no small feat, BBVA’s Chávez concedes. Operationally, BBVA’s major challenge was transforming vast amounts of unstructured data into a clean, corporate-grade knowledge base.
“We had to implement rigorous data governance to ensure the agent’s ‘brain’ was fueled only with accurate, up-to-date information,” he notes.
Chang Li, chief manager, Nippon Life Insurance
And while agentic AI has generated significant enthusiasm, there are, as yet, only isolated examples of success, and tangible value across financial services remains limited. Ambiguous strategic objectives, organizational complexity, and the challenge of replicating interpersonal dynamics represent critical barriers, says Chang Li, chief manager, Nippon Life Insurance Company, director of the Fintech Association of Japan, and ambassador for FinCity.Tokyo.
“First, we must understand what we’re looking to achieve, whether that’s better customer communication or cost cutting,” she says. But defining strategy and purpose is difficult for any one division alone; it requires collaboration between departments, Li notes, since bureaucratic structures often prevent meaningful conversations between the correct stakeholders.
Are there concerns about agentic AI taking over from humans in some finance functions? That may no longer be the right question, Li says: “I think it’s more useful to think about the conditions under which the first human ‘channel’ might be taken over by AI and consider how companies should prepare for that.”
The necessary degree of trust is not yet in place for agentic AI to truly replace humans in banking, however. “Currently, agentic AI is only feasible for the information collection step,” says Li, with an agentic contract still “a few years” off.
For BBVA, building trust into agentic AI systems is foundational. “In the financial sector, trust is our most valuable currency,” says Chávez. The bank proactively aligns with demanding emerging standards, including frameworks from Europe and the US, in addition to Peruvian regulations.
“This ethical stance has directly shaped our strategic roadmap,” he notes. “We’ve prioritized decision support use cases over autonomous decision-making. We started where AI assists and humans validate. It’s the most responsible way to deliver immediate value while mitigating risks and building the trust needed for deeper automation.”
In an era of falling revenues, financial institutions may find the productivity gains they need from agentic AI, McKinsey suggests, predicting that early adopters will secure a lasting advantage over slow movers: but not overnight.
McKinsey anticipates a breakout agentic business model will emerge in the next three to five years and is urging bank executives to focus on a small number of high‑value workflows, such as frontline sales, account planning, and financial close processing; define clear guardrails for agent autonomy; and invest early in data quality and risk controls to ensure pilots can scale safely: all with “surgical precision” in identifying the potential earnings impact.
