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Hello and welcome to Eye on AI. In this edition….Google launches the ability to make purchases directly from Google Search’s AI Mode and Gemini…Apple selects Google to power an upgraded Siri…Meta announces a new AI infrastructure team…researchers use AI to find new ways to edit genes.

It was another week with a lot of AI-related announcements. Among the bigger news items was Google’s launch of an e-commerce shopping checkout feature directly from Google Search’s AI Mode and its Gemini chatbot app. Among the first takers for the new feature is retail behemoth Walmart, so this is a big deal. Behind the scenes, the AI checkout is powered by a new “Universal Commerce Protocol” that should make it easier for retailers to support agentic AI sales. Google Cloud also announced a bunch of AI features to support agentic commerce for customers, including a new Gemini Enterprise for Customer Experience product that combines shopping and customer support (watch this space—the combination of those two previously separate functions could have big implications for the way many businesses are organized.) Home Depot was one of the first announced customers for this new cloud product.

It’s still early days for agentic commerce, but already many companies are panicking about how they make sure their products and sites surface highly in what these AI agents might recommend to users. A nascent industry of companies has sprung up offering what are variously called “generative engine optimization” (GEO) or “generative-AI optimization” (GAIO) services. Some of these echo longstanding internet search optimization strategies, but with a few key differences. GEO seems, at least for now, somewhat harder to game than SEO. Chatbots and AI agents seem to care a lot about products that have received positive earned media attention from reputable news outlets (which should be a good thing for consumers—and for media organizations!) as well as those that rank highly in trusted customer review sites.

But the world of AI-mediated commerce presents big governance risks that many companies may not fully understand, according to Tim de Rosen, the founder of a company called AIVO Standard, which offers companies a method for generative AI optimization and also a way to track and hopefully govern what information AI agents are using.

The problem, de Rosen told me in a phone call last week, is that while various AI models tend to be consistent in how they characterize a brand’s product offerings—usually correctly reporting the nature of a product, its features, and how those features compare to competing products and can usually provide citations to the sources of that information—they are inconsistent and error-prone when asked questions that pertain to a company’s financial stability, governance, and technical certifications. Yet this information can play a significant role in major procurement decisions.

AI models are less reliable on financial and governance questions

In one example, AIVO Standard assessed how frontier AI models answered questions about Ramp, the fast-growing business expense management software company. AIVO Standard found that models could not reliably answer questions about Ramp’s cybersecurity certifications and governance standards. In some cases, de Rosen said, this was likely to subtly push enterprises towards procurement decisions involving larger, publicly traded, incumbent businesses—even in cases when a privately-held upstart also met the same standards—simply because the AI models could not accurately answer questions about the younger, privately-held company’s governance and financial suitability or cite sources for the information they did provide.

In another example, the company looked at what AI models said about the risk factors of rival weight loss drugs. It found that AI models did not simply list risk factors, but slipped into making recommendations and judgments about which drug was likely the “safer choice” for the patient. “The outputs were largely factual and measured, with disclaimers present, but they still shaped eligibility, risk perception, and preference,” de Rosen said.

AIVO Standard found that these problems held across all the leading AI models and a variety of different prompts, and that they persisted even when the models were asked to verify their answers. In fact, in some cases, the models would tend to double-down on inaccurate information, insisting it was correct.

GEO is still more art than science

There are several implications. One, for all the companies selling GEO services, is that GEO may not work well across different aspects of brand information. Companies shouldn’t necessarily trust a marketing tech firm that says it can show them how their brand is showing up in chatbot responses, let alone believe that the marketing tech company has some magic formula for reliably shaping those AI responses. Prompt results may vary considerably, even from one minute to the next, depending on what type of brand information is being assessed. And there’s not much evidence yet on how exactly to steer chatbot responses for non-product information.

But the far bigger issue is that there is a moment in many agentic workflows—even those with a human in the loop—where AI-provided information becomes the basis for decision making. And, as de Rosen says, currently most companies don’t really police the boundaries between information, judgment, and decision-making. They don’t have any way of keeping track of exactly what prompt was used, what the model returned in response, and exactly how this fed into the ultimate recommendation or decision. In regulated industries such as finance or healthcare where, if something goes wrong, regulators are going to ask for exactly those details. And unless regulated enterprises implement systems for capturing all of this data, they are headed for trouble.

With that, here’s more AI news.

Jeremy Kahn
jeremy.kahn@fortune.com
@jeremyakahn

FORTUNE ON AI

Anthropic launches Claude Cowork, a file-managing AI agent that could threaten dozens of startups—by Beatrice Nolan

U.K. investigation into X over allegedly illegal deepfakes risks igniting a free speech battle with the U.S.—by Beatrice Nolan

Malaysia and Indonesia move to ban Musk’s Grok AI over sexually explicit deepfakes—Angelica Ang

Anthropic unveils Claude for Healthcare, expands life science features, and partners with HealthEx to let users connect medical records—by Jeremy Kahn

AI IN THE NEWS

Apple chooses Google’s AI for updated Siri. Apple signed a multi-year partnership with Google to power key AI features in its products, including a long-awaited Siri upgrade, the companies announced on Monday. The deal underscores Google’s resurgence in AI and helped push the market value of Google-parent Alphabet above the $4 trillion threshold. Apple said the agreement does not change its existing partnership with OpenAI, under which Siri currently hands off some queries to ChatGPT, though it remains unclear how the Google tie-up will shape Siri’s future AI integrations. The financial terms of the deal were not disclosed either, although Bloomberg previously reported that Apple was considering paying Google as much as $1 billion per year to access its AI models for Siri.

Meta announces new AI infrastructure team, including former Trump advisor. The social media giant said it was creating a new top-level initiative called Meta Compute to secure tens—and eventually hundreds—of gigawatts of data center capacity. The effort is being led by Daniel Gross, a prominent AI tech executive and investor who Meta had hired to help its Superintelligence Labs effort, and Santosh Janardhan, who is the company’s head of infrastructure. CEO Mark Zuckerberg said the way Meta builds and finances data centers will become a key strategic advantage, as the company pours money into facilities such as a $27 billion data center in Louisiana and nuclear-power partnerships to meet energy demand. Meta also named Dina Powell McCormick, who served in several key positions during the first Trump administration, as president and vice chair to help forge government partnerships and guide strategy, reporting directly to Zuckerberg. You can read more from the Wall Street Journal here.

Microsoft warns that DeepSeek is proving popular in emerging markets. Research published by Microsoft shows that U.S. AI companies are losing ground to Chinese rivals in emerging markets. The low-cost of open models built in China, such as DeepSeek, is proving decisive in spurring adoption in places such as Ethiopia, Zimbabwe, and Turkmenistan. Microsoft president Brad Smith said Chinese open-source models now rival U.S. offerings on performance while undercutting them on price, helping China overtake the U.S. in global usage of “open” AI, especially across Africa and other parts of the global south. By contrast, U.S. firms like OpenAI, Google, and Anthropic have focused on closed, subscription-based models—raising concerns that without greater investment, the AI divide between rich and poor countries will widen, and that U.S. companies may ultimately see their growth limited to more developed markets. Read more from the Financial Times here.

Salesforce launches updated Slackbot powered by Anthropic’s Claude. Salesforce is rolling out an upgraded Slackbot for Business+ and Enterprise+ customers that uses generative AI to answer questions and surface information across Slack, Salesforce, and connected services like Google Drive and Confluence. The new Slackbot is powered primarily by Anthropic’s Claude model. The company says the AI assistant respects user permissions and is designed to reduce reliance on external tools such as ChatGPT by working directly inside Slack, which Salesforce acquired for $27.1 billion in 2021. The launch comes as investors remain skeptical about enterprise software firms’ ability to benefit from the AI boom, with Salesforce shares down sharply over the past year despite its push to get businesses to adopt its “Agentforce” AI agents. Read more from CNBC here.

EYE ON AI RESEARCH

Microsoft, Nvidia and U.K. startup Basecamp Research make AI-aided breakthrough in gene editing. An international research team including scientists from Nvidia and Microsoft has used AI to mine evolutionary data from more than a million species to design potential new gene-editing tools and drug therapies. The team developed a set of AI models, called Eden, which were trained on a vast, previously unpublished biological dataset assembled by Basecamp. Nvidia’s venture capital arm is an investor in Basecamp.

The AI models can generate novel enzymes for large, precise gene insertions that could improve the ability of the body’s immune cells to target cancerous tumors. Basecamp has demonstrated the effectiveness of these gene-edited cells in laboratory tests so far, but they have not been tested in people. The Eden-designed gene editing enzymes can also make genetic edits that allow cells to produce peptides that can fight drug-resistant bacteria. Researchers say the work could dramatically expand the range of treatable cancers and genetic diseases by overcoming long-standing data and technical constraints in gene therapy. Experts caution, however, that the clinical impact will depend on further validation, safety testing, and regulatory and manufacturing hurdles. You can read more from the Financial Times

AI CALENDAR

Jan. 19-23: World Economic Forum, Davos, Switzerland.

Jan. 20-27: AAAI Conference on Artificial Intelligence, Singapore.

Feb. 10-11: AI Action Summit, New Delhi, India.

March 2-5: Mobile World Congress, Barcelona, Spain.

March 16-19: Nvidia GTC, San Jose, Calif.

BRAIN FOOD

What if people prefer AI-written fiction, or simply can’t tell the difference? That’s the question that New Yorker writer Vaudhini Vara asks in a provocative essay that was published as a “Weekend Essay” on the magazine’s website a few weeks ago. While out-of-the-box AI models continue to struggle to produce stories as convincing as graduates of top MFA programs and experienced novelists, it turns out that when you fine-tune these models on an existing author’s works, they can produce prose that is often indistinguishable from what the original author might create. Disconcertingly, in a test conducted by researcher Tuhin Chakrabarty— who has conducted some of the best experiments to date on the creative writing abilities of AI models—and which Vara repeats herself in a slightly different form, even readers with highly-attuned literary sensibilities (such as MFA students) prefer the AI written versions to human-authored prose. If that’s the case, what hope will there be for authors of genre fiction or romance novels?

I had a conversation a few months ago with a friend who is an acclaimed novelist. He was pessimistic about whether future generations would value human-written literature. I tried to argue that readers will always care about the idea that they are in communication with a human author, that there is a mind with lived experience behind the words. He was not convinced. And increasingly, I’m worried his pessimism is well-founded.

Vara ultimately concludes that the only way to preserve the idea of literature as the transmission of lived experience across the page, is for us to collectively demand it (and possibly even ban the fine-tuning of AI models on the works of existing writers.) I am not sure that’s realistic. But it may be the only choice left to us.

FORTUNE AIQ: THE YEAR IN AI—AND WHAT’S AHEAD

Businesses took big steps forward on the AI journey in 2025, from hiring Chief AI Officers to experimenting with AI agents. The lessons learned—both good and bad–combined with the technology’s latest innovations will make 2026 another decisive year. Explore all of Fortune AIQ, and read the latest playbook below: 

The 3 trends that dominated companies’ AI rollouts in 2025.

2025 was the year of agentic AI. How did we do?

AI coding tools exploded in 2025. The first security exploits show what could go wrong.

The big AI New Year’s resolution for businesses in 2026: ROI.

Businesses face a confusing patchwork of AI policy and rules. Is clarity on the horizon?



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As corporate earnings soar and the U.S. GDP balloons, the American workforce isn’t feeling the same boom. American workers are taking home less of the country’s overall wealth, data from the Bureau of Labor Statistics show, and employment in the U.S. is set to continue to slow.

Labor share, or the portion of the U.S.’s economic output that workers receive through salary and wages, decreased to 53.8% in the third quarter of 2025, its lowest level since the BLS started recording this data in 1947, according to its labor productivity and costs report published last week. In the previous quarter, labor share was at 54.6%. This decade, the labor share average was 55.6%.

That’s despite corporate earnings skyrocketing, with profits for Fortune 500 companies hitting a record $1.87 trillion in 2024. The U.S. GDP grew 4.3% in the third quarter last year, exceeding economists’ predictions. 

That growth has not only come at the expense of how much of the pie of wealth workers are taking home, but also how many Americans are in the workforce, economists warn.

“That decline in the share of labor has got to be either falling earnings or falling numbers of people,” Raymond Robertson, a labor economist at Texas A&M’s Bush School of Government, told Fortune. “The falling share of income is having to do with the shift towards capital.”

Indeed, there are growing signs that as national income balloons, the U.S. workforce is deflating. Unemployment ticked down to 4.4% in December, but still sits above the 4.1% rate from 12 months before. Moreover, employers added just 584,000 jobs in 2025 compared to 2 million added in 2024.

The stark bifurcation of corporate victories and weak labor data raises concerns among economists of jobless growth jeopardizing the U.S. workforce, as well as a K-shaped economy, where the rich get richer while the poor get poorer, becoming more exaggerated.

“Data right now is very mixed,” Robertson said. “But I think it also all consistently points to this idea that things are getting worse for workers and much better for billionaires.”

Making sense of jobless growth

Robertson attributes weakening labor share averages to the rise in automation, which he noted is displacing workers, with productivity—a metric essentially measuring worker output—continuing to rise. Third-quarter GDP data showed nonfarm productivity growth soared to an annualized rate of 4.9%.

“All these things, bit by bit, are replacing people, and they’re concentrating income and their share of capital,” he said.

Goldman Sachs analysts Joseph Briggs and Sarah Dong estimated in a report this week, based on Department of Labor job numbers, that AI automation could displace 25% of all work hours. They predicted that over the course of the AI adoption period, a 15% increase in AI-driven productivity would displace 6% to 7% of jobs, and, at its peak, a 1 million increase in unemployed workers.

The displacement is substantial, the analysts said, but said the impacts of automation will be tempered by a wealth of new jobs created as a result of the technological changes.

Automation is expected to be a boon to corporate profits and GDP, expected to boost GDP by 1.5% by 2035, according to a Wharton brief published in September 2025. Early signs indicate AI is already driving productivity gains, with companies who invested $10 million or more in AI reporting significant productivity gains compared to organizations investing less in the technology, according to EY’s U.S. AI Pulse Survey.

Robertson added that growing unemployment, which he expects to see rise over the next few months, keeps wages down, allowing margins and profits to expand.

To be sure, the recent productivity surge has been an “open question,” Morgan Stanley economists wrote in a note to clients this week, not unanimously attributed to increased adoption of AI or automation. The analysts suggested this increase would be cyclical, or vestigates of pandemic-era habits of companies making more from less.

An Oxford Economists research brief published earlier this month suggested companies are disguising overhiring-related layoffs as a result of AI, but said automation-related workforce reductions have not yet happened en masse. Additionally, while unemployment has been ticking up over the past year, it is still relatively low.

An immigration crackdown backfires on U.S. labor

Mark Regets, senior fellow at National Foundation for American Policy, sees a different reason for a slowing workforce. He told Fortune President Donald Trump’s immigration crackdown has not done what Trump administration officials, such as White House Deputy Chief of Staff Stephen Miller, said it would in increasing the number of U.S.-born workers. Instead, according to Regets, Trump’s immigration policies have not only decimated the foreign-born workforce, but has also created fewer opportunities for domestic-born workers to find jobs.

The most recent BLS household survey reveals a decline of 881,000 foreign-born workers since January 2025, and a decline of 1.3 million workers since a March 2025 peak, consistent with the Congressional Budget Office’s report last year indicating shrinking U.S. population growth as a result of migrants being deported or refusing to come to the U.S. out of fear of hostile polities.

“The data is raising huge red flags that we are losing immigrants of all types that we otherwise would be advancing America’s economy,” Regets said.

The rising U.S. unemployment rate, up from 3.7% in December 2024 is counterevidence to Miller’s argument that harsher immigration policy would grow the U.S. workforce, he added. In fact, fewer immigrant workers may actually make it harder for U.S.-born individuals to find work.

“A company unable to find the workers it needs for some roles could shut down operations rather than continuing,” Regets said.

He noted that skillset diversity in a workplace could boost productivity and justify employing more people. Greater immigration can also increase consumer spending and stimulate businesses, as well as encourage businesses to take advantage of ample labor market availability and seek out their labor instead of offshoring jobs.

Reversing a shrinking labor force

While friendlier immigration policies could help reverse an exodus of foreign-born workers, Robertson said addressing the workplace automation push would be key to growing the U.S. workforce.

“There are trades that are technology-assisted,” he said. “Those are going to be in higher demand, but you really still have to have a significant investment in skills.”

The young generation of workers are already prepared to adapt to a changing labor landscape. Gen Z are flocking to trade schools in hopes of a finding a job as a carpenter or welder not so easily outsourced by AI, and in 2024, enrollment in vocation-based community colleges increased 16%, according to data from the National Student Clearinghouse. 

Companies have taken it upon themselves to provide reskilling opportunities to employees. An Express Employment Professionals-Harris Poll survey from 2024 found that 68% of hiring managers intended to reskill employees at some point during the year, up from 60% in 2021. While the U.S. Department of Labor updated guidelines to encourage states to adapt workplace development systems, Robertson argued the government hasn’t done enough in several decades to imbue the workforce with necessary skillsets for future jobs.

“Democrats and Republicans have not significantly invested in training [or] the retraining or active labor market programs that you need to match workers to jobs,” Robertson said. “That’s the obvious solution.”

Without changes, economists see the pattern of an employment slowdown continuing, but with greater concern about the ability for the U.S. economy to sustain growth.

“We need job growth to have a growing economy, and I think we need job growth to pay our debts,” Regets said. “I don’t know how you have job growth with a shrinking labor force.”



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Chief people officers—and Jamie Dimon—say AI can’t learn ‘human skills.’ The world’s youngest self-made billionaires want to prove them wrong

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Leaders like JP Morgan CEO Jamie Dimon argue that EQ and critical thinking are the only skills that will survive the automation wave. Microsoft Satya Nadella would agree, calling emotional intelligence a required workplace skill. These statements are meant to give workers reassurance that AI won’t completely replace people, highlighting an irreplaceable human trait that the technology supposedly cannot acquire. The stakes are high, with some AI thought leaders such as Dario Amodei warning that half of all entry-level white-collar jobs will disappear, and soon, amid the AI wave.

But a Silicon Valley startup is challenging the assumption that human judgment is off limits to AI.

Mercor, a San Francisco-based AI firm, is hiring people from a vast list of professional career backgrounds to improve its AI, training the model to adopt core skills in a more human-like manner. In other words, they are building a business to prove executives like Jamie Dimon and  Satya Nadella wrong—and to hasten the replacement of people with AI in the workforce, closing the last mile of human employment.

The company’s CEO Brendan Foody and co-founders Adarsh Hiremath and Surya Midha were recently minted the youngest self-made billionaires after the company was valued at $10 billion last November. That funding has given the 22-year-olds the resources needed to build out their ambitious AI venture.

Mercor’s mission is to bridge the gap between machine learning and human nuance. “Everyone’s been focused on what models can do,” Foody told Fortune in November. “But the real opportunity is teaching them what only humans know—judgment, nuance, and taste.”

The shift toward high-skilled gig work is a response to a volatile labor market where even professional skills aren’t enough to ensure a worker’s job security. According to the World Economic Forum’s 2025 Future of Jobs Report, employers estimate that 39% of core skills — such as problem-solving and communication — will be disrupted by 2030, with 40% of firms planning to reduce their workforce specifically due to AI automation. As entry-level white-collar roles begin to vanish, the demand for specialized knowledge and “human-in-the-loop” expertise have become critical currency for workers seeking to resist automation.

Simple work, fast money

Mercor’s career page lists dozens of job postings for contract work looking for individuals with subject-area expertise, including investment banking and private equity analysts, linguists, sports journalists, soccer commentators, astronomists and legal experts. 

The job postings offer hourly rates ranging from $10 for bilingual experts to as much as $150 for finance experts. Aside from competitive pay, the job’s perks include fully remote work. Mercor’s website claims an average hourly rate of $86, with about $2 million paid out to experts daily.

To apply, all applicants must do is submit an initial application followed by an AI interview tailored based on area of expertise, which is then reviewed by Mercor staff. Once hired, contractors evaluate how well their AI system completes micro-tasks — such as writing a financial memo or drafting a legal brief — using detailed rubrics to grade the AI’s performance. This allows for the AI to learn how people make decisions.

The company says it hired 30,000 contractors last year, with 80% being US-based, according to a Mercor spokesperson. The work day varies as contractors have no set hours. Some log 10 hours per week, others work 40 or more, with specific projects lasting weeks or months.

The Wall Street Journal recently found some of the humans who are teaching AI how to do the difficult, human-skill-heavy tasks in which they are experts. “I joked with my friends I’m training AI to take my job someday,” Katie Williams, 30, told the Journal. Williams, who has a background in news and social-media marketing, has worked at Mercor for about six months, watching videos and writing out transcripts of what happens in them, and rating the quality of videos generated by prompts.

The quest for nuance

The company’s newly launched AI Productivity Index, or Apex, benchmarks AI models on real-world knowledge in four fields: medicine, management consulting, investment banking and law. The system uses the same rubric and expert-generated tasks that its contractors help to create, grading models on their production ability. 

The index found that even the most advanced models, like GPT-5, failed to meet the “production bar” for autonomous work. GPT-5 achieved a top score of 64.2%, with scores varying for each category and scoring as low as 59.7% in investment banking.

Despite being far from perfect, the company says that AI models performing at 60% or better can reshape the nature of work as professionals work in tandem with the technology. “Perhaps a consultant can more easily complete a competitor analysis if given an initial draft from an AI,” the company wrote. As AI continues to evolve, the most human skill may no longer be doing the work, but possessing the right judgment required to critique it.



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‘Hybrid creep’ is the latest trick bosses are using to get workers back in the office

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“Hybrid creep” is emerging as the newest way employers are nudging remote workers back to their desks, one extra office day and perk at a time rather than through blunt mandates. Framed as flexibility and culture-building, the quiet shift is reshaping what “hybrid” really means in 2026.​

The phrase, which appears to have been coined by the Boston-based videoconferencing software maker Owl Labs in its 2025 state of hybrid work report, describes a slow, often unspoken expansion of in‑office expectations, where a nominal two- or three-day schedule gradually tilts toward a de facto full-time presence. With formal policy changes largely failing to bring workers back by stick, the carrot that companies are turning to is more like a combination of social pressure, subtle incentives, and performance signals to pull workers back in.​​ The Wall Street Journal‘s Callum Borchers, who reported on the phenomenon, argued it’s a particularly passive aggressive form of workforce management, designed to raise office attendance without issuing a direct order.​

The tactics bosses are using

Hybrid creep often starts with adding more “anchor days,” as noted by Stylist, or days when teams are expected in the office for meetings, collaboration sessions, or client visits. Over time, those anchors spread across the week, making it harder for employees to keep meaningful work-from-home days.​

Promotions and plum assignments increasingly flow to the people who show up the most, sending a clear signal visibility matters as much as—or more than—output. At the same time, companies roll out social perks—free lunches, events, guest speakers—to make the office feel like the center of professional life again.​​

Many managers complain they still struggle to measure productivity and mentor staff they rarely see, especially younger workers learning on the job. Hybrid creep offers a way to restore in‑person oversight and informal coaching while avoiding the public relations hit of a strict mandate.​

This new species of hybrid creeper comes after several varieties of pandemic-era fauna flourished in the jungle of remote and hybrid work. The “coffee badger,” the millennial-tilted hybrid worker who swiped their badge in just long enough to have the proverbial cup of java before heading for the hills of the home office, may regard the hybrid creeper as their natural predator. The “job hugger,” on the other hand, the worker who discovered a new sense of loyalty to their employer after the “Great Resignation” curdled into the “Great Stay” and now the “no-hire economy,” will surely be amenable to the onset of hybrid creep.

Owl Labs found the coffee badger is thriving, at 43% of the workforce, but so is the silent creature of “hushed hybrid,” with 17% of hybrid workers having remote arrangements they don’t openly discuss. These findings align with what commercial real estate giant Jones Lang LaSalle termed the “non-complier” who is “empowered” to make their own schedule, out of some kind of value provided to the company.

Some employees welcome clearer routines and in‑person contact after years of scattered hybrid arrangements. For others, hybrid creep feels like a broken promise, eroding the flexibility that led them to accept or stay in a job in the first place.​

Critics warn tying advancement to badge swipes can punish caregivers, disabled workers, and those with long commutes, even when their performance is strong. Employee advocates also argue opaque expectations breed resentment, fueling quiet quitting or renewed job searches as workers realize the ground rules have changed.​

Career coaches advise workers to document results and press managers for explicit expectations—how many days in office, which days, and how that links to performance reviews. Clarity, they argue, is the best defense against a creeping requirement that never appears in writing but strongly shapes careers.​

For employers, the risk is over-reliance on hybrid creep will damage trust if workers feel manipulated rather than consulted. As the fight over where work happens enters another phase, the future of hybrid work may hinge less on policy documents and more on these quiet, incremental pushes back to the office. The Journal‘s Borchers noted hybrid creep is nearing a tipping point, as the badge-swipe company Kastle Systems’ back-to-work barometer has posted year-over-year gains in each of the past six months, and over 50% attendance is the norm as of early 2026, a new high over 2025’s attendance.



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