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.

Yet, Monday came, and while gold and silver went vertical, equities stayed calm and the dollar barely drifted. To economist Tyler Cowen, the renowned libertarian from George Mason University and author of the influential Marginal Revolution blog, this lack of market panic is the most revealing part of the drama. It isn’t that investors trust the administration’s motives; it’s that they have already accepted the “ugly little truth” that the Federal Reserve’s independence is a relic of a bygone era. 

“What Trump did was terrible,” Cowen said on the technology podcast TBPN, referring to the administration’s erratic, “Captain Queeg” style of institutional pressure. “But to me, the reason markets didn’t react more is because we already wrecked the independence of the Fed. That’s the ugly little truth behind this story. It was already wrecked.”

In Cowen’s telling, the damage was done years ago, through fiscal policy. Budget deals, tax cuts and a chronic deficit have steadily narrowed the Fed’s real freedom to act, regardless of its formal mandate.

“The basic problem is our debt and deficits are so high that over time, we will monetize them to some extent and have higher inflation because we prefer that over higher taxes, no matter what we might say,” Cowen said on technology show TBPN.

That preference, Cowen argues, quietly undermines central bank independence. Even without overt political pressure, a heavily indebted democracy is one that limits its own monetary choices. At some point, inflation becomes the least politically painful way to manage obligations that voters are unwilling to finance through taxes or spending cuts.

A grim echo

This diagnosis is a grim echo of the work of Ray Dalio, the billionaire founder of top hedge fund Bridgewater Associates, who has long warned of the “Big Cycle” debt trap. Dalio’s framework suggests that nations with massive debts eventually run out of good options. They are left with a choice between three politically poisonous options: austerity (massive spending cuts), default (which would be unthinkable for a reserve currency), or inflation (“printing money” in order to devalue the debt). 

Dalio has frequently agreed with Cowen that for the United States, inflation is the only path forward, since it is an invisible tax that a democracy will always prefer over the political suicide of massive tax hikes or the gutting of social programs. Speaking with fellow billionaire, Carlyle co-founder David Rubenstein, Dalio recently said, “My grandchildren, and great grandchildren not yet born, are going to be paying off this debt in devalued dollars.”

Cowen offered a prediction about how what Dalio calls the “ugly deleveraging” will look: the U.S. may require half a decade of 7% inflation to erode the debt’s value relative to the size of the economy.

“It’s highly unpleasant, and a lot of people will be thrown out of work and living standards will be lower,” Cowen said. “But we’ve already spent that money. We can’t default, and that’s what’s facing us over the next 10 to 15 years,” implying that, while default would ordinarily be a country’s way out of this kind of dilemma, America’s status as the richest economy in world history and the home of the world’s reserve currency make that unfeasible.

The irony, Cowen notes, is that America’s unique status allows it to run higher debt than almost any other nation, even the wealthy ones. That privilege may boost living standards today, but it still weakens political discipline tomorrow, allowing leaders to not only “get away with more debt” but also explicitly destabilize the Fed without worrying too much about market backlash. 

Although neither Dalio nor Cowen have taken this argument about the debt into the feud between Powell and Trump, at its heart lies a similar dynamic: how can the U.S. improve living standards for its lower and middle class? Trump has been badgering Powell about interest rate cuts that would bring down mortgage rates and ease housing affordability, but that runs the risk of fueling an even higher inflation wave down the road, or sooner. 

Albert Edwards, an outspoken and eccentric global strategist for Societe Generale, sounded eerily similar to Dalio and Cowen when he spoke to Fortune in November. “We’re going to end up with runaway inflation at some point,” Edwards said, “because, I mean, that’s the end game, right? There’s no appetite to cut back the deficits.”

The god out of the machine

There is, however, a deus ex machina that could change the course of things: the productivity miracle that many economists expect to come, driven by artificial intelligence. If AI could boost U.S. GDP growth by a full percentage point per year, Cowen said, the country might grow its way out of the debt trap without resorting to a decade of high inflation. Yet he is skeptical. 

Roughly half the U.S. economy—government, higher education, much of healthcare, and the nonprofit sector—is structurally sluggish, he argues. AI may save workers enough time in these sectors to “hang out more at the water cooler,” but not enough to dramatically raise output. Meanwhile, innovation might just concentrate at already-productive sectors of the economy. Without a radical efficiency gain in the half of the economy that doesn’t produce “white or black-belt” AI tools, the debt clock will continue to outrun the AI revolution.

The result is a new, more dangerous era for the U.S. dollar.

“I’m not telling you not to worry” about Fed independence, Cowen said. “I’m telling you should have been worried to begin with.”

And yet, as Morgan Stanley noted in early January, something else appears on the calculus along with the latest rumbles about central bank independence: a 4.9% boost to annualized productivity, as suggested by fresh third-quarter GDP data. 

“We believe much of the rise is cyclical,” economists led by Michael Gapen noted, adding “it remains an open question as to what is driving the productivity acceleration.”

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?