If you’ve been paying attention to AI at all lately, you’ve certainly seen the “Something Big Is Happening” essay by Matt Shumer, or at least some of the reaction to it. In it, Shumer describes how coding, for him, has completely transitioned from manually writing code to simply prompting and approving the near-flawless work done by AI. The piece was meant as a warning to all knowledge workers, essentially saying: AI has taken over my job, and it’s coming for yours next.
There have been countless thought pieces on the merits and flaws of Shumer’s argument, and I have no intention of adding to the pile. But journalism is knowledge work, too, and the field had its own, slightly less viral, moment of AI existential crisis this past week.
The editor of Cleveland.com, Chris Quinn, wrote a column this week, describing how a college student who had applied for a reporting job withdrew their application when they found out how the publication uses AI. Besides using AI to help generate story ideas, the newsroom developed an “AI rewrite specialist” to write stories based on the material that reporters gather. By ditching writing, according to Quinn, their reporters have been able to reclaim an extra workday each week.
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The backlash was predictably vicious. On X, Axios reporter Sam Allard earned a lot of likes by comparing what Cleveland.com is doing to being an “AI content farmer,” while various veteran journalists on Substack expressed various degrees of outrage and dismay. Most of the reaction was along the lines of this piece from journalist Stacey Woelfel: “Writing is an integral part of the reporting process.”
The AI newsroom split
That’s true, but I think what Quinn describes isn’t so easily dismissed. After all, reporters often work in teams on single articles, with one of them taking the lead on the draft. Did the others then . . . not report? And I’ve certainly been in breaking-news situations where a reporter would text, email, or call in their notes to an editor or writer who would put together the piece.
It’s generally recognized that writing and reporting are different skills, and what Quinn and Cleveland.com appear to have done is use AI to fully separate them. The conventional wisdom on the “correct” way to use AI is to let it take over the tasks that it can do faster and better than humans, freeing them up to do the things that absolutely require human engagement and judgment. In the case of a reporter, that’s talking to sources, learning new things, and earning their trust.
Well, at long last, AI is actually very good at writing. Certainly, much of the text that’s come out of AI systems over the past few years hasn’t done much for its literary reputation (yes, we’re all tired of the rampant em-dashes and the “it’s not Xit’s Y” bits). But if you use the most powerful models with a modest amount of deliberate prompting, they can produce highly competent prose.
And if we’re being honest, highly competent prose is all that’s needed for a large amount of reported stories. Many, if not most, news reports are meant to convey basic information about what happened, with little judgment or opinion, and typically written in AP style, which is essentially a formula. It’s not quite code, but it’s a very functional way of writing. The most important thing is conveying the facts, accurately and with context, as quickly as possible.
Again, it’s important to understand that the reporter is not removed from the process, but their role changes significantly. Just as Shumer found himself becoming a supervisor to an AI building machine, reporters may become operators of writing bots, ensuring they’re crafting stories properly out of the raw material they’ve been given. In the case of Quinn’s newsroom, reporters have final say over the copy.
Bleeding between the lines
None of this is to say this approach will result in a perfect future. There are writers who aren’t great at reporting, and there are reporters who aren’t skilled at writing, but there are plenty who are good at both. Will they need to pick a sideeither become a feature or opinion writer, or settle for just doing the reporting part?
And what about skill building? Even if this approach is as successful as Quinn says, how will junior staff become better writers without the day-in, day-out act of writing stories? When Woelfel says writing is integral to reporting, I think he means it’s integral to storytelling, which is an act of curation, prioritization, and expressionall with an audience in mind. This is what Ben Affleck meant when he famously drew a distinction between AI as a craftsman and AI as an artist. But how do you become an artist if AI is doing all thecrafting?
The irony of Shumer’s piece is that, while he makes a solid case that AI will soon disrupt most knowledge workand even name-checks journalism as one of the areas in the crosshairshe did it with an essay with a distinctly human voice. I honestly don’t know if he used AI to fully or partially write the piece, but I’m certain that if he did, he also was meticulous about every word.
I think that’s a hopeful sign that, even if we relegate some of the craft of writing to AI, that we might not lose as much as we might think. Audiences will always demand a human touch, so that touch will need to manifest in some form. It’s true that no one wants to read AI slop. But it might turn out that the most valuable reporting skill in the future will be the ability to turn slop into stories.
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As snow piled up in front of bus stops and fire hydrants during New York City’s second winter storm of the year, city workers have tried to move fast to remove it before snow hardened into ice. A new internal tool makes that job easier to track.
The city’s Department of Sanitation (DSNY) now tags infrastructure that’s been plowed in a mobile mapping tool that employees can update on the go.
“We have started the work of geotagging every single bus shelter and crosswalk,” Mayor Zohran Mamdani said Monday, and overnight, he said the city cleared more than 1,600 crosswalks, 419 fire hydrants, and nearly 900 bus stops.
[Screenshot: Courtesy of New York City Department of Sanitation]
DSNY handles trash collection, but it’s also tasked with snow removal from city streets and bike lanes, areas within its legal obligation. DSNY sometimes provides supplemental services too, plowing pedestrian infrastructure like curb ramps, unsheltered bus stops, and fire hydrants that property owners are responsible for.
In the past, this supplemental work was done piecemeal, but under Mamdani, the amount of supplemental service has “vastly increased,” says Joshua Goodman, a DSNY deputy commissioner. “That necessitated a need to formally track this work,” he says.
[Screenshot: Courtesy of New York City Department of Sanitation]
Cities from Bellevue, Washington, to Syracuse, New York, use digital maps to show residents when streets get plowed, and New Yorkers can track when their streets were last plowed on PlowNYC, a public site launched in 2013. DSNY needed its own PlowNYC, but for bus stops and more.
“We developed an internal mapping tool, and Sanitation Supervisors make live updates from the field when one of these locations in their assigned section is complete,” Goodman tells Fast Company. “So maybe it’s a bit simpler than the terminology impliesit’s essentially someone making updates to a central database on their work cell phonebut it’s a big development for us, especially so quickly.”
“This is our first storm using it, but it is allowing greater efficiency around clearing these important areas,” he adds.
[Screenshot: Courtesy of New York City Department of Sanitation]
Preparations began following the snowstorm in January, when sites were surveyed for the mapping tool. The interface looks like a typical maps app, and while perhaps simpler than what the idea of “geotagging” might conjure, the database of information the tool stores is vast. New York City has about 13,000 bus stops and about 83,000 crosswalks in commercial corridors. The tool was designed by the DSNY operations management division, which is its data and analytics team.
To handle snow from the latest storm, DSNY has delayed trash and recycling collection so its workers can prioritize snow removal, and it’s hired hundreds of emergency temporary snow shovelers for $30 per hour. That’s a pop-up snow shoveling army with tens of thousands of sites and miles of ground to cover. Tracking this work with clipboards wouldn’t be efficient. By developing an internal tool to better monitor their job, DSNY found a quick solution to solve a pressing problem.
For decades, a legal degree felt like a golden ticket, a safe career choice because a robot could never take a lawyers job. Today consumers are increasingly turning to new technologies like generative artificial intelligence for answers to their legal questions without the assistance of a lawyer.
No wonder: The high cost of legal services places them beyond the reach of most Americans. Some outside the profession see this market failure as an opportunity. Legal technology startups armed with AI agents are securing billion-dollar evaluations, and after recent leaps in AI models and new featuresincluding one from Anthropic that can help automate legal taskssome legal and tech stocks went into shock. The sense that “something big is happening” also left at least some lawyers wondering whether the robots are finally coming for their jobs, and asking if this is the beginning of the end of the legal profession?
It doesnt need to be. Lawyers could try to wage what will certainly be a losing campaign against the encroachment of new technologies on areas of American life typically dominated by lawyers. Instead, the American legal profession can learn to run with the machines and not against them, fulfilling their ethical duty to ensure all Americans have access to justice by harnessing these technologies to deliver affordable and accessible legal services at scale.
These two powerful phenomenathe emergence of new technologies and the fact that tens of millions of Americans face their legal problems without a lawyerwill certainly encourage Americans to rely on new and widely available tools regardless of whether the information and guidance these consumers receive is accurate. And it often isnt. Indeed, according to one recent report, there are nearly 1,000 documented cases of lawyers and unrepresented litigants referencing fictitious court decisions and other legal authorities in court filings because of AI hallucinations: instances where the AI fabricated the legal sources upon which those litigants relied to their detriment, resulting in fines and other punishments from the courts.
The tragic reality driving many Americans to these imperfect alternatives to professional legal help is not that consumers are choosing between a lawyer and a bot; they are all too often facing their legal problem with no lawyer at all. This is especially true in areas where the fees available to lawyers are low, yet the stakes for the consumer high: where a tenant faces eviction, an immigrant is at risk of deportation, a homeowner might lose their home to foreclosure, or a victim of identity theft faces a mountain of debt they did not accumulate themselves. Roughly 93% of low-income and half of middle-income Americans go without adequate legal representation when confronted with legal problems like these.
This access-to-justice crisis, as bad as it is, leads to larger and even more troubling concerns. When lawyers are not available to vindicate important interests, that threatens other critical values all Americans should cherish: individual liberty and dignity, civil rights, equal justice, and the rule of law.
But this isnt the first time that the legal profession has faced these sorts of challenges. At the turn of the 20th century, industrialization led to reorganization of the bar into larger and larger law firms to respond to the growing and more complex demands of their clients. Simple technologies like the telephone, telegraph, and typewriter made the practice of law more efficient, allowing lawyers to provide more comprehensive services to their corporate clients.
Ironically, many of the measures elites in the bar formulated to respond to these societal and technological changes led to the current market failure. Indeed, instead of welcoming more lawyers into the profession to meet the growing need for its services, elites in the bar erected barriers to entry where few existed before (at least if you were white and male). They built high walls and wide moats to prevent dilution of the legal services market, including requiring an expensive legal education and more challenging bar exams before an aspiring lawyer could begin to practice.
These requirements had the desired effect: limiting access to the profession and artificially inflating the cost of legal services. What is more, many of these barriers endure and continue to drive up the cost of legal services today.
This time is different though. Never before has it looked like technology could truly displace lawyers. Indeed, tools like CitizenshipWorks, an online portal that helps individuals apply for citizenship, and Depositron, which assists tenants in New York seeking a return of their security deposit from their former landlords, are meeting critical needs without the fees a lawyer might otherwise charge for these services. Think of it as the expansion of TurboTax-like products to other areas of the law.
There are certainly situations where there is no substitute for a living, breathing lawyer, like when a criminal defendant is facing a felony charge, or when a complex and novel business transaction requires unique legal skills. But when the alternative is no legal representation at all, as is the case with far too many American consumers with far simpler legal needsneeds that can be met through technological innovationsthe profession has an obligation to find ways to address those needs, even when doing so will bring down the price of legal services or displace some traditional legal jobs. In the face of such threats to their position in society, however, lawyers must remember that the point of the legal system is not to serve as a full-employment plan for lawyers; it is to help people solve their legal problems.
This market opportunity is one that lawyers can actually seize. Instead of ignoring new technologies or erecting even higher walls to their adoption, the legal profession should embrace and shape these technologies, creating an array of options for individuals, families, and businesses to address their legal problems at lower cost, and at scale.
Big Law is already adopting many of these new tools to serve their well-heeled clients; the present cost of building effective systems may mean that the widespread adoption of such technologies at the high-end of the legal services market actually makes the access-to-justice gap worse, not better.
Instead of exacerbating legal access inequality, the profession should build bridgesaided by new technologiesthat will span the chasm between those who require legal assistance and those who can afford it, even if the services that solve Americans legal problems in the not-so distant future are not always delivered by lawyers alone.
Theres plenty of legal work to go around. Lawyers should be the ones figuring out how to put new technologies to useto serve the legal needs of all Americans in creative, ethical, effective, affordable, and accessible ways. When they do that, they will serve the professions most important values and functions, and advance what should be its highest ideals.
Communicating on group chats has quickly become a way of life, but what are the rules?We used to use email, the phone or talk in person. Now we use platforms like iMessage, WhatsApp or Slack to coordinate a night out with friends, a kids’ birthday party, a work project or even to discuss sensitive military information as Defense Secretary Pete Hegseth did by sharing details of airstrikes in a Signal chat.But while group chats have exploded in popularity because of their informality, that also creates its own challenges: discussions can veer off topic, repetitive or basic questions can irritate group members, and that viral meme you think is funny could also offend.The principles of digital etiquette remain the same as other kinds of etiquette, but they are also “context specific and many of the rules are implicit rather than explicit,” said Rupert Wesson, a director at Debrett’s, the British etiquette guide, who outlined key tips for The Associated Press:
Think before messaging
Etiquette is always based on the idea of care and consideration for others, Wesson said. So it helps to think about how the recipients might be affected by your message.That means, for example, not wasting other members’ time by asking questions that could be easily answered by doing a Google search, or scrolling up or searching through the previous posts.The Trent Windsurfing Club near Nottingham, England, which communicates with members using both WhatsApp and email, spells out other considerations in a 15-point list on its website.“Don’t get angry if someone doesn’t respond to your messages in a group. No one is obliged to do so. Better send him/her a direct message,” the club says.Also, “Before sending a video, picture, meme or any content, analyze if such material will be in the interest of the majority of the members of the group.”And avoid sending videos or files that are very large, because “nobody likes to saturate the memory of their smartphone or waste their data/internet plan on nonsense,” its guidance says. The club did not respond to a request for comment.
Remember the aim of the chat
Always consider the chat group’s purpose. For those created with a specific and practical function in mind, just stick to the task and don’t post any more than you need to, Wesson said.On the other hand, “some groups are there for frivolity and here, more is more,” he added.It should be obvious, but don’t post personal stuff in a company or business-related chat, and refrain from posting work-related material in a group with friends or family.It doesn’t hurt to lurk first before weighing in, partly because on some chat platforms new members can’t see what was posted before they joined.“It is always best to err on the side of caution until you are very clear on the purpose and culture of the group,” Wesson said.
Consider the size of the group
Do you need to respond to every message? There’s often someone who feels the need to type out a reply to every post, even if it’s just to say “thanks.” But doing so in a big group might be somewhat akin to an email reply-all storm.Wesson advises considering how many people are in the chat.“If there are three of you in the group, a response, if only an emoji, is almost expected,” Wesson said. “In group of 50 or more it is practically a criminal offense.”
Keep it clean and decent, especially at work
This is an especially important point when it comes to work communications, with many white collar workers now using chat platforms like Slack and Microsoft Teams rather than email to communicate.These platforms feel less formal than email but don’t forget to follow the same guidelines as you do with other company communications.“Assume anything messaged can be forwarded and be especially cautious of work chats (however informal they appear),” Wesson said. “As countless people have discovered at employment tribunals, any diversion into anything indecorous can be career limiting.”
Less can be more in chats
Chat messages should be short and sweet.One reason is that your words could come across differently depending on the person reading the message, so stick to using short sentences to avoid being misinterpreted.If it’s about work, and you want to discuss something in more length and detail, consider an in-person meeting, a phone call, or email instead.“No one wants to read a 7-inch-long unformatted message when an organized attachment would have worked better,” the experts at The Emily Post Institute the American equivalent of Debrett’s advised in a blog post on business communications.
Message clarity and style matter
It’s not a college essay, so the rules around grammar, punctuation or even emoji don’t need to be too strict.“You should not feel too constricted and nor should you judge others for playing fast and loose with the King’s English,” Wesson said. “Just let brevity and clarity be your guide.”Speaking of emoji, they’re fun and can convey your meaning as well as the most thoughtful turn of phrase, Wesson said. But don’t abuse them because they can be a “minefield.”There’s a world of difference between, for example, the crying emoji and the crying with laughter emoji, he said. It’s best to play it safe and avoid emoji when, for example, sending condolences, Wesson said.
How to properly leave a chat group
If you’re getting annoyed by the number of message notifications from a big chat group, or you feel uncomfortable because of some of the comments, just put it on mute. And don’t be afraid to leave the group if you don’t need to be in it.Before leaving, consider letting the chat administrator know.“The group administrator has a responsibility to ensure the chat serves its purpose and that things don’t get too out of hand,” Wesson says.What should admins do if certain people are causing problems?“If things are going awry, deleting a member is an option but perhaps a little drastic. A quiet DM or a brief muting should always be considered first,” Wesson says.
If you do leave the chat, should you say farewell?
Again, it depends on the context. If it’s for a one-off event with a lot of people you don’t know, there’s probably no need.But if, say, you’re part of a remote work project, it would be a good idea to notify everyone.“When leaving make it clear that you are removing yourself immediately so the chat does not fill up with people wishing you farewell,” Wesson said.
Is there a tech topic that you think needs explaining? Write to us at onetechtip@ap.org with your suggestions for future editions of One Tech Tip.
Kelvin Chan, AP Business Writer
The stock prices of the so-called Quantum Four are back on the rise today, after already accruing significant gains yesterday as well.
The upward trend is a reversal for IonQ, D-Wave, Rigetti, and Quantum Computing Inc., which have all seen their shares decline since the beginning of the year.
Why are they on the rise again? Heres what you need to know:
Whats happened?
Yesterday, the stock prices of Americas four largest publicly traded quantum computing companies all rose significantly. As of yesterdays close, heres where the quantum computing companies stock prices stood:
IonQ, Inc. (NYSE: IONQ): up 6.23% to $33.59
D-Wave Quantum Inc. (NYSE: QBTS): up 5.31% to $19.65
Rigetti Computing, Inc. (Nasdaq: RGTI): up 6.98% to $17.63
Quantum Computing Inc. (Nasdaq: QUBT): up 7.03% to $8.68
But the upward momentum hasnt stopped there. Currently in premarket trading, the stock prices of all four quantum companies are surging again.
According to Yahoo Finance data, as of the time of this writing in premarket trading, the four companies stock prices are up the following amounts:
IONQ: up around 15%
QBTS: up around 7%
RGTI: up around 5.5%
QUBT: up around 5.5%
The last two trading days have marked (so far) a dramatic reversal for the stock prices of these four companies. Since the beginning of the year, shares of all four companies had tumbled between 15% and 25% as of yesterdays close.
From a great 2025 to a rough 2026
No doubt about it, most quantum computing stocks had a great run in 2025. During the year, most saw their prices surge.
IonQ rose from around $32 in the beginning of the year to over $73 per share by September.
D-Wave saw its price jump from around $6 per share in early 2025 to over $46 by October.
Rigetti saw its stock go from around $9 per share in early 2025 to as high as $58 in October.
Even Quantum Computing Inc., which started the year down significantly from its 2024 highs, at around $9 in January 2025, saw its price surge to more than $25 by late September.
But by late 2025, the mood surrounding quantum stocks had shifted.
Yes, individual investors began feeling more apprehensive about the prospect of massive returns, particularly after the stock performed so well throughout most of 2025, because quantum computing is still a nascent technology with few real-world use cases yetmeaning massive profits for these small companies are still years away.
But quantum stocks also took a hit due to factors beyond the companies control, particularly thanks to general fears of a worsening economy, an increasingly likely AI bubble, and uncertainty around inflation, interest rates, and geopolitics.
These factors cause many investors to pull money from highly volatile assetsincluding AI stocks, cryptocurrencies, and, yes, quantum stocksand park it in safe-haven assets like gold and bonds to protect gains they had made in those volatile assets until that point.
This quantum selloff continued into 2026. Even with yesterdays stock price rises, the Quantum Four have declined significantly year to date.
Why are quantum stocks back on the rise?
The recent share price rise indicates that investor appetite for quantum computing stocks is also on the rise again. The question is why?
The most likely answer seems to point to one quantum computing company in particular: IonQ. Yesterday, the Maryland-based company announced its fourth-quarter 2025 financial results.
And those results equated to a blowout quarter.
For Q4 2025, IonQ announced revenue growth of 429% year over year. In Q4 2024, IonQ brought in $11.7 million in revenue, and in Q4 2025, that revenue leaped to $61.9 million.
The company also reported a 202% rise in full-year fiscal revenue. For its fiscal 2025, IonQ brought in $130 million in revenue, up from $43.1 million in 2024.
Of course, those revenue numbers are still small change compared to what the legacy tech and AI giants bring in every quarterbut they do show that there is growing demand for the quantum technologies that IonQ is developing.
As for why the three other Quantum Four companies are seeing their stock prices rise too, its likely a case of a rising tide lifts all boats.
Historically, when one of the Quantum Four announces good news (such as revenue increases), the stock prices of the other companies tend to rise.
The investor logic behind this is that if there is increasing demand for quantum technology solutions, that demand will likely benefit all companies operating in the space.
Of course, its worth noting that quantum stocks are still highly volatile, so while they are up significantly over the past 24 hours, theres no guarantee that the bumpy ride for Quantum Four stocks is over.
For most of modern business history, accounting has been something leaders looked at periodically. Numbers were reviewed and reports arrived on a schedule (often monthly, quarterly, or at tax time). Accounting happened when there was time, not necessarily when insight was needed.
Across industries, a new model is taking shape: always-on accounting. These are systems that capture financial activity continuously, organize it automatically, and surface insights in real time.
While this shift is relevant everywhere, its especially visible in the rental housing market where millions of small, independently run businesses (often managed by individuals or families who balance day jobs) are adopting operating standards once associated only with large enterprises.
Ive experienced this evolution from multiple sides. Ive been a renter, an investor, and the cofounder and CEO of a property management software company, and over the past decade Ive worked closely with thousands of small rental business owners. Across all of those perspectives, Ive been at the forefront of a major shift reshaping how these businesses operate: Accounting is no longer something thats checked on periodically; its something that runs continuously.
FROM PERIODIC REVIEW TO CONTINUOUS AWARENESS
Traditional accounting systems relied heavily on manual processes. Financial data lived across spreadsheets, folders, and disconnected tools. Capturing expenses, categorizing transactions, and reconciling accounts took hours of focused work, which meant most businesses reviewed their numbers periodically due to time constraints.
As accounting technology has matured, many of the tasks that once demanded human attention now happen automatically. Transactions flow in without manual entry. Expenses are captured at the moment they occur. Data is categorized consistently in the background. The systems themselves maintain continuity.
That technological shift enables a behavioral one. Instead of treating accounting as something to revisit at set intervals, business owners can operate with continuous awareness. Financial insight doesnt need to be reconstructed; its already there. Patterns surface naturally over time. Trends become clearer, not because leaders are checking more often, but because the information is always current.
Weve seen similar transitions before. Cloud infrastructure replaced periodic system checks with real-time monitoring. Analytics platforms turned marketing into an ongoing feedback loop. Finance is now following the same path, moving from static snapshots to living systems.
WHY RENTAL HOUSING IS LEADING THE WAY
Small rental businesses are a revealing example of this shift. They operate at the intersection of entrepreneurship and long-term asset ownership, often run by people balancing full-time careers, families, and other responsibilities. Efficiency matters. Clarity matters even more.
For years, accounting was the last major workflow to modernize. Rent collection became digital. Communication went mobile. But financial tracking often remained manual, fragmented, or delayed.
Modern property management software systems now pull in transactions automatically, extract data from receipts, categorize expenses by property, and generate up-to-date profit-and-loss views with minimal effort. What once required hours of administrative work now happens effortlessly in real time.
The result is more than cleaner records; its confidence. When financial insight is always current, owners engage with their businesses differently. They spend less time wondering if something was missed and more time understanding what the numbers are telling them.
ACCOUNTING AS INFRASTRUCTURE, NOT PAPERWORK
One of the most meaningful mindset shifts Ive observed is how accounting is now viewed: not as paperwork, but as infrastructure.
Always-on accounting supports operations the way reliable connectivity or modern logistics do. It reduces friction, minimizes human error, and creates a single source of truth that decisions can build on.
In rental housing, this has implications beyond individual businesses. Small operators collectively provide homes for millions of people. When they run with better financial visibility, theyre better positioned to maintain properties proactively, manage cash flow sustainably, and navigate economic shifts with steadiness.
What looks like operational efficiency at the business level often translates into stability at the community level.
A BROADER LESSON FOR SMALL BUSINESSES EVERYWHERE
Whats happening in rental housing reflects a broader trend across the economy. Small businesses are increasingly adopting systems that assume continuity rather than reminders, thanks to automation technology that works without constant prompting.
The best tools today dont ask owners to remember tasks or reconcile gaps later. They capture activity automatically and organize it intelligently. This shift reduces cognitive load, which is one of the most underappreciated constraints on entrepreneurship.
When leaders arent mentally tracking loose ends, they have more capacity for strategy, creativity, and long-term thinking. Always-on accounting frees attention. And attention is one of the most valuable resources any business has.
BETTER SYSTEMS CHANGE BEHAVIOR, NOT JUST WORKFLOWS
The most interesting impact of always-on accounting isnt speed. Its behavior.
When people trust their numbers, conversations change. Instead of asking whether the data is complete, they focus on what it reveals. Better-informed decisions can be made earlier with greater confidence.
In rental businesses, this often means treating properties less like side projects and more like durable enterprises. In other industries, the takeaway is similar: Clarity reshapes leadership.
Accounting becomes something owners engage with at a higher levelreviewing trends, comparing periods, and planning next movesrather than something they have to reconstruct under pressure.
THE FUTURE OF SMALL BUSINESS ACCOUNTING IS ALWAYS-ON
The future of accounting wont be defined by louder dashboards or more complex reports. It will be defined by systems that are always on, working continuously in the background.
In rental housing, that means books that are always current and decisions that are always informed. In the broader business landscape, it signals a shift toward tools that support momentum instead of interrupting it.
Always-on accounting represents a larger evolution in how small businesses operate: fewer fire drills, more foresight; less reconstruction, more understanding.
Confidence doesnt come from having more data; it comes from knowing the data is already there when you need it. And as more businesses adopt systems built for continuity, that confidence is becoming the new standard.
Ryan Barone is cofounder and CEO of RentRedi.
For decades, digital transformation raised hopes of simpler work. And while many companies found complexity instead of clarity, the story isnt over. AI brings a new wave of hope and energy, and with that, a new kind of tension.
Whenever I connect with business leaders, I can feel their deep optimism and sincere sense of responsibility to deliver on AI transformation. Leaders want to boost productivity and stand by their people. Theyre guiding teams through uncertainty while inspiring them to embrace change.
Thats why AI transformation is a people challenge as much as a tech challenge. Org charts are shifting. Roles are evolving. And the new priority for leaders is equipping people with the skills and wisdom to adopt AI and power this transformation with confidence.
Leaders can do this with a three-step playbook:
1. BUILD THE HR ORGANIZATION FOR THE FUTURE.
If HR operating models dont evolve, leaders are asking their people to build the future on quicksand. AI demands new ways of working, which is why HR leaders are stepping into hybrid positions that mirror the real world of work, where talent and technology are intrinsically linked.
That starts by making HR and IT true partners so they can co-create AI experiences that solve real business problems. Fifty-five percent of organizations have launched 100+ AI use cases, but only 19% are tracking how those use cases impact business goals. Siloed efforts cant scale.
Thats why weve built what we call an AI Factory, a model to collect, evaluate, and prioritize use cases quickly and ethically, at scale, across the business. Employees have submitted thousands of AI use case ideas. About 100 of them have gotten past our prioritization frameworkmeaning we believe they can deliver ROI, safely and at scaleand weve prioritized about a quarter that demonstrate the most value.
As technology evolves, so must the roles around it. Leaders need to imagine new roles that move HR from an administrative function to a strategic hub. Think AI orchestration designers, or AI ethics officers. These roles are tailored to the companys business needs and critical to a people-centric AI transformation.
2. ENABLE AI ACROSS THE ORGANIZATION AND RESKILL WITH URGENCY.
AI is already increasing what employees can do and changing their daily tasks. To lead through change, we need to understand not just what people need to learn, but how they learn best.
It starts with the concept of an AI heatmap to identify which tasks can be automated or augmented and quantify potential gains. That insight helps leaders rethink how they grow and support their teams.
By using AI and data, we can map current skills, surface gaps, and design targeted, real-time development paths. Then, we need to do the hard work: Train people to know, work with, build, and lead with AI.
Weve built an AI-native learning model through ServiceNow University to do just that. Our goal is to train 3 million learners by 2027.
And this isnt just a nice-to-have. The skills gap is real. According to the World Economic Forums 2025 Future of Jobs report, 63% of employers see it as a major barrier to transformation. If we dont close this gap now, well never realize AIs full potential.
3. TRANSFORM THE WORKFORCE LIKE ITS YOUR FULL-TIME JOB (IT IS!).
Leaders are steering through massive change. Some employees will fear the unknown. Organizations that invest in an agile, resilient workforce, one person at a time, will win the AI race.
Thats why leaders need to take an X-ray of their organizationnot just charts and systems, but a deep look at the workforce structure, skills, and capacity to grow. Then, they can start closing gaps and ensuring AI is adopted in a way thats human at the core while fueling business growth.
Old org charts need a rebrand. Work is more dynamic and cross-functional. And now, we have AI working alongside people. Because of this, we need to move beyond traditional, linear models of change management toward continuous, adaptive, and decentralized change readiness.
This agentic AI workforce will require thoughtful planning, human wisdom, a focus on well-being, and a strong culture at the core. Thats why collaboration and orchestration are critical. If leaders get this right, they can unlock new business models and real growth.
THE RESULT? VALUE
Leaders who follow these steps can supercharge business results while avoiding the pitfalls that slow AI adoption. At ServiceNow, we track adoption and ROI through our AI Control Tower, a real-time measurement that creates a flywheel of value: unlock time, reinvest it, and grow faster.
The opportunity is clear: Embrace AI, lead with confidence, and bring people along the journey. The organizations that thrive will help people and AI technology co-create, not just coexist.
Jacqui Canney is chief people and AI enablement officer of ServiceNow.
There are a few odors from adolescence that are seared into the brains of most Americans who grew up after the 1980s: the aroma of freshly baked brick pizza in the school cafeteria, the acrid stink of a locker room, and the unmistakable scent of teen boys wearing an unforgivable amount of Axe body spray.
The phenomenon of teens dousing themselves in Axe has become so ubiquitous since the brand’s founding in 1983 that over the past few years it’s inspired its own subgenre of memes (see this one and this one, for example).
Now Axe has its sights set on a new generation of consumers with a redesigned spray mechanism for its signature product.
To mark the occasion, on February 20 the brand announced its self-referential History of Overdoing It campaign. Axe has always been part of the cultural conversation around guys doing too much, and for years that included how our body spray was used, Dolores Assalini, head of Axe U.S., said in a press release.
[Photo: Axe]
At last, Axe is offering a solution. According to the Unilever-owned brand, overspraying was always a design problemand to fix it, the team has invented new spray technology to keep offensive odors at bay.
The Axe bottle gets a facelift
Brajin Vazquez, senior manager of DEO formats technology at Unileverand one of the minds behind the Axe redesignsays the chronic overspraying of Axes old product was influenced by a few factors of the bottles design. The formulation of the spray, combined with the design of the bottles valve and nozzle, resulted in a thick, diffused cloud of fragrance, creating that classic overpowering smell.
For years, weve heard that while people liked the fragrances, Axes spray could feel too heavy or create too much of a cloud, Vazquez says. That feedback made us look closely at the delivery system itself. We realized that improving the user experience wasnt just about messaging, it required updating the spray technology.
[Photo: Axe]
Vazquezs team started by rethinking the products ingredients. They reduced the amount of propellant gas in the spray and added nitrogen to the mix, which, Vazquez explains, made room for a higher proportion of liquid formula and created space in the formulation to increase odor-control actives and deliver more fragrance per spray. Essentially, this means that users can spray less of the product and still get the same body-odor-masking effect.
This new formulation is combined with a reengineered spraying system. The old design, Vazquez says, operated at a high pressure, which resulted in a stronger, higher-velocity spray. The new valve component mitigates the problem by keeping the sprays flow light. The bottle also features a spray insert with a nozzle opening thats 25% smaller than the old version, allowing users to apply the fragrance to more targeted areas without that dreaded cloud effect.
Realistically, Axe’s retooled design probably won’t solve chronic overspraying altogetherbut at least now there are some guardrails in place for a problem that’s plagued middle school hallways for decades.
[Photo: Axe]
Below, Tom Griffiths shares five key insights from his new book, The Laws of Thought: The Quest for a Mathematical Theory of the Mind.
Griffiths is a professor of psychology and computer science at Princeton University and director of the Princeton Laboratory for Artificial Intelligence.
Whats the big idea?
How can we study something we cant see or touch? Mathematics allows us to develop rigorous theories about how minds work. It also lets us use those theories to build artificial intelligence systems. Just as physicists seek to identify Laws of Nature, cognitive scientists hope to discover the Laws of Thought.
Listen to the audio version of this Book Biteread by Griffiths himselfin the Next Big Idea app.
1. The story of AI goes back hundreds of years.
For many people, AI seems to have come out of nowhere. In late 2022, it suddenly became possible for anyone to have a conversation with chatbots that could draw on more knowledge than any human. Dig a little deeper and you might discover that the approach behind those chatbotsbuilding bigger and bigger artificial neural networkshad its first dramatic demonstration in 2012, when it was used to significantly improve how well computers identify images. But the story goes back much further than that.
When Enlightenment thinkers, like René Descartes or Gottfried Wilhelm Leibniz, first began using mathematics to effectively describe the physical world around us, they also suggested that the same kind of approach might be used to describe the mental world inside us. Those early efforts led to the development of mathematical logic and digital computers, which in turn led to the creation of cognitive science by psychologists who used mathematical ideas to come up with new theories about the mind. Modern AI springs from that tradition: Key advances in the development of artificial neural networks came from psychologists seeking to understand how the human mind works.
2. No single piece of mathematics describes the mind.
Cognitive scientists started using mathematical logic to describe thought, but after a couple of decades realized that wasnt going to work. Concepts have fuzzy edges that logic just cant capture. Artificial neural networks were developed in parallel and became much more powerful after a group of psychologists showed how they could be used to learn more complex relationships than anyone had thought possible.
Continuing to scale up those neural networks takes us to modern AI. But understanding how neural networks learnand how to create systems that learn more like peoplerequires a different approach, one that uses ideas from probability theory. These three mathematical traditions intertwine to give us a more complete picture of how the mind works.
3. Crucial discoveries come from pursuing unpopular ideas.
The first neural networks that could learn were built by a computer scientist who abandoned the project after deciding that, in order for them to learn anything interesting, they would have to be much larger than he considered practical. But a psychologist worked out how to make them learn better, which caused a lot of excitement about the potential of that approach. However, that same computer scientist then showed that even those neural networks had fundamental limitations, and they decreased in popularity.
A decade later, some psychologists became interested in neural networks as tools for understanding human cognition, cracked the problem of how to get them to learn more complex relationships, and neural networks became popular again. Then, machine learning researchers became interested in the statistical foundations of learning, and neural networks decreased in popularity. Soon, more powerful computers and larger datasets made it possible to use neural networks to solve even more challenging problems, bringing us to the present day.
This back-and-forth between disciplineswhere an unpopular idea in one discipline is picked up and improved upon by researchers in another disciplineis a nice illustration of how an interdisciplinary field like cognitive science can have a huge impact.
4. We are closer than ever to understanding the human mind.
I used to tell my students that cognitive scientists have made a lot of progress in figuring out how to ask questions about the mind, but were still a long way from having answers. But now, the progress in AI over the last decade is beginning to suggest answers to some of our deepest questions about human intelligence.
Mathematical frameworks like logic and probability theory are fundamental to describing the nature of thought and learning, but the abstract rules and inferences they identify need to be implemented in real human brains. Artificial neural networks give us important hints about how that might work. Putting these pieces together gets us remarkably close to fulfilling the vision that Descartes and Leibniz had centuries ago of having a mathematical framework for describing thought.
5. There are still big differences between human minds and AI.
Despite all that progress, modern AI still has some important gaps. One of the biggest regards learning. If you read aloud all of the text that is used to train todays chatbots, it would take tens of thousands of years. By contrast, a human child learns to be a fluent speaker of their native language in less than 10 years. That means that theres something in human brains that is different from what is inside our AI algorithms. Figuring out what that might be is a problem that we study in my lab, and a preoccupation of many cognitive scientists.
There are also interesting questions about what exactly it is that artificial neural networks are learning, and whether they represent the world in the same way as us. In some cases, they may be, but in others, we can show that they are quite different. Figuring out what AI systems know and when they are likely to succeed or fail at a task is a great opportunity to use the methods that cognitive scientists have honed by studying humans. For a long time, we have only had one species that demonstrated this kind of intelligent behavior, so having another one to study opens the door to not just understanding more about AI but understanding more about ourselves.
Enjoy our full library of Book Bitesread by the authors!in the Next Big Idea app.
This article originally appeared in Next Big Idea Club magazine and is reprinted with permission.
Your colleagues decide in less than a minute whether your email is worth replying to. Microsofts 2025 Work Trend Index Report shows that the average employee receives 117 emails a day, and most are skimmed in under 60 seconds. In other words, if your email takes someone more than a minute to understand, theres a strong chance you wont be getting a timely response.
Well-written emails dont just make you sound smarter; studies show that they also reduce misunderstandings and speed up responses.
Here are five simple ways to get faster email responses, while also helping your recipient preserve mental energy and time.
BREAK UP WITH THE EMAIL BRICK
Long blocks of text are the enemy of attention. Research shows that visually uncluttered text (with white space and intentional spacing) is easier for busy readers to scan and digest quickly. Simply formatting your email with bullet points, bold text for important questions or updates, and short paragraphs will significantly increase your chances of getting a prompt response.
Structure is just as important as length. If the email is longer than this article, consider your reader overwhelmed.
DONT LEAD WITH SMALL TALK
One of the biggest mistakes professionals make is burying the lead. Instead of opening with a short anecdote or unrelated small talk (Hope your week is going well), start with the purpose of your email, and ideally, the action you need. In military and executive communication, this is known as BLUF (Bottom Line Up Front).
BLUF requires you to put key information, like the request or decision needed, in the first sentence or two. After you have led with the key information, you can share further details that the recipient can read if they need background context.
And yes, you can still ask your coworker if they have plans for the weekend or how their dog is doing. But for the sake of everyones sanity, leave this to the end.
DONT PLAY EMAIL TENNIS
The back-and-forth dance of unanswered questions (When works for you?, Morning works, What time?) costs time and demands cognitive switching. One survey of modern workplace behavior found that knowledge workers spend roughly 28% of their workweek managing email, with a large portion of that time simply waiting on or chasing down replies. To reduce this, try to include all relevant details on the first send.
One way to address this is, if youre proposing a meeting, include your availability windows, the purpose of the call, and how long you expect it to take in a single message. If you want a call back, include your direct phone number rather than waiting for the other person to ask.
Write a clear subject line
In a crowded inbox, the subject line acts as a decision filter: Is this relevant? Is this urgent? Can this wait? Studies show that email subject lines critically influence whether a recipient opens, defers, or ignores an email (before theyve read a single sentence of the message).Do your best to craft a subject lines that are specific, concise, and action-oriented. For example, Budget Review Needed by 3 PM is more effective than a generic phrase like Quick Question.
BUILD EMAIL TRUST
If you teach people over time that your emails are concise and to the point, you are building email trust. This means that recipients are more likely to respond positively and quickly when they see your name. Researchers in written communication emphasize that consistency in formatting and clarity doesnt just improve readability, it builds an implicit reputation for professionalism.
Getting faster email replies isnt about sounding smarter. Its about making decisions easier for the person on the other side of the screen. When your emails are clear, scannable, and consistent, you reduce mental load, build trust, and teach people to respond to you faster.
