While most teams have managers and team leads, many also have something less official, but just as recognizable: the workplace parent.
Theyre the go-to for advice . . . even for things that may not even be related to work. They remember birthdays, organize celebrations, and somehow have everything you might need.
Paper clip? No problem. Jumper cables? Of course. The phone number for the receptionist youre too scared to calldont worry, they did it for you.
But what does it really mean to be the caretaker of your workplace? And can that caring nature sometimes hold you back professionally? Here are four signs that youre the workplace parent, plus the risks . . . and how to pull back if needed.
Youre the one who has everything for everybody
Jamie Jackson has been an HR professional for 21 years. She says she herself has been known to be the workplace parent, and that theyre not too hard to spot: look for the person regularly doling out batteries, a Band-Aid, Tylenol, she says.
Jackson explained that when cleaning out her old office, she realized just how much shed leaned into the role. I had things like birthday candles, a lighter, every kind of pain reliever you can think of, she says. Oh, you dont take ibuprofen? No worries, Ive got Aleve and Tylenol.
For her, it wasnt just about being preparedit was about making sure everyone around her felt supported.
I dont think its necessarily just an HR thing, she says. I just want to make sure people are taken care of and have what they need. If it meant me having a few of these things in my desk at all times, I was going to do it.
Youre the go-to helper
Another way to spot a workplace parent is by how often people turn to you for guidance or advice.
If they know they can trust you to help them, then you’re probably the workplace parent, Jackson says.
It often shows up in the small momentswhen colleagues seek your help on something theyre unsure about or just need someone to listen.
A clear sign? When a colleague comes to you saying, I need help. I dont know what to do. And you hand them a tissue box, close the office door, and just let them vent.
Youre in charge of the fun
Being the workplace parent often means being the fun committee for the office.
You might be the person who remembers all the little things, like colleagues anniversaries or what kinds of pets they have.
At the beginning of every month, Id check whose birthday was coming up, get the cards ready, make sure they were signed, and send them off a few days before. Not too early, because I didnt want it to feel forced, Jackson says.
Or you might be the default event organizer, planning happy hours, team celebrations, even bridal or baby showers. I was often the one saying, Lets do this in the break room, she adds.
While being a workplace parent is an honorable, nurturing role, it can come with some drawbacks.
Why do people do this?
According to organizational psychologist Erica Pieczonka, a workplace parent often stems from a better-known term: people-pleasing.
A people pleaser measures their self-worth by being helpful to others; what motivates them is being helpful, Pieczonka says. This might look like someone who simply cant say no, or the moment a coworker needs help, theyre already jumping in with a solution or offering to fix it.
The behavior could come from a fawn response someones had since childhood, in which theyre constantly trying to please authority figures for validation.
Being the go-to helper can quietly sabotage your career if youre not careful.
Sometimes it distracts you from your real job, Jackson says. While admirable, it can become risky if the president starts to wonder, What does that lady actually do? Jackson notes.
Pieczonka says workplace parents often end up neglecting their own career goals because theyre so focused on everyone elses. They may also struggle with delegation. They might feel like, If I ask somebody else to do this, it’s going to be a burden to them, so I need to do it myselfor think, It’s easier for me to just do it.
On top of that, they wind up carrying the teams emotional load. Theyre the ones scheduling social gatherings, and the people colleagues go to when they have emotional issues.
Even in situations where they need to give criticism, they may hold back.
They’ll often soften it or pull back because they don’t want to hurt somebody’s feelings, Pieczonka says. But then, the other person doesn’t benefit from really understanding how they could improve. By constantly solving others problems, workplace parents inadvertently create dependency, keeping colleagues from learning to tackle challenges themselves.
The workplace parent is taking away the challenge, Pieczonka explains.
This pattern can accumulate over time, making it harder to sustain performance and satisfaction at work.
Burnout is my biggest concern, Pieczonka says.
The Fix? Boundaries
Jackson started protecting her time by scheduling support instead of providing it on demand.
If someone stopped by in crisis mode, shed offer, Todays not a good day. But what if I give you 15 to 20 minutes tomorrow? she explains. Often, people would sleep on it and no longer need to talk.
And when someone insisted on immediate hand-holding, shed shift into tough-love mode: This is a big-boy, big-girl job, shed say. Youve got to take charge and handle it.
Pieczonka adds that setting boundaries starts with understanding your own capacity. Ask yourself: Really, where am I investing time? Is this the right investment of time, and what are my true priorities? she says.
She also recommends asking before assuming someone needs your help. A lot of workplace parents assume that they have to be the person to help, or that the person wants their help, but they may not.
If you find yourself doing this, ask yourself: Am I the right person to help right now? Do I know this person needs my help?
Finally, she emphasizes reframing self-care as strategic rather than selfish. Workplace parents can feel selfish taking care of themselves because their worth is tied to helping others, but you have to fill your own cup.
Schedule itfive minutes of meditation, a walk, a workout, whatever you needand treat it as nonnegotiable on a weekly basis.
Being the workplace parent comes from a good place, but protecting your time and setting boundaries ensures you can keep helping otherswithout losing yourself in the process.
When AI wearable company Friend blanketed New York City with ads last month, there was significant backlash. Many of the company’s ads (which included rage-baiting copy like, Ill never bail on our dinner plans) ended up defaced with graffiti that called the product AI trash, surveillance capitalism, and a tool to profit off of loneliness.
Despite the campaign running in New York, it struck a national nerve as it became a lightening rod for people’s feelings around AI. It was only a matter of time before the brands got in on the debate.
A couple weeks after the campaign’s debut, beer giant Heineken joined the chat, posting on Instagram: The best way to make a friend is over a beer. It touted its own social wearablea bottle openerthat bears a striking resemblance to the AI-powered Friend necklace.
[Image: Heineken US]
Now, the brand has turned that into a new outdoor ad campaign around New York, adding that the brand has been social networking since 1873. Created with agency Le Pub New York, it is a silly poke at the NYC-centric zeitgeist for Heineken. But its also the latest in a consistent string of work by the brand over the years that has aimed to remind people to put down their phones and log off social media in favor of IRL social interaction.
The new ads feature the hashtag #SocialOffSocials, harking back to the Social Off Socials campaign the brand launched in April. Built around the premise that adults spend too much time online, but also feel trapped in a vicious cycle of social media addiction, it starred Joe Jonas, Dude with Sign, Lil Cherry, and Paul Olima.
For that campaign, Heineken commissioned a study of 17,000 adults in the U.S., U.K., and seven other international markets and found that more than half of adults feel overwhelmed keeping up-to-date with social media. And nearly two-thirds say they are nostalgic for the 1990s when there were no smartphones.
More social, less social media
Earlier this year in South Africa, the brand created an installation in a mall so that people watching soccer on their phones alone could actually combine their screens to make one giant, collective viewing experience.
The brand also created a limited edition phone case called The Flipper, that would flip your phone over to screen down when it heard the word, Cheers.
Meanwhile, last years The Boring Phone tapped into the dumb phone trend among Gen Z. Created with streetwear retail brand Bodega, Heineken made 5,000 Boring Phones to give away. But the message is very much the same: It’s time to ditch the phone for a real social life.
I reached out to both Heineken and Le Pub for comment, and to find out if the Friend-like bottle openers will be available to the public. This story will be updated as soon as I hear back.
As tech companies shell out millions for top AI talenteven reportedly billionsregular rank-and-file employees are left wondering how to get in on the action and land a job in artificial intelligence.
One report found that job postings that mention needing at least one AI skill had salaries 28% higher than other jobs, which translates to $18,000 more. Jobs that required two AI skills had a 43% salary jump.
To begin with, its worth considering where the AI jobs are and how this intersects with your interests and existing skills. Many jobs in AI can roughly be divided into five different categories:
researchers
engineers
business strategists
domain experts
policymakers
Researchers bring a deep understanding of neural networks and algorithm design to the table and can push the technology forward, but this is a very small pool and typically requires a PhD.
Engineers typically have programming skills that they can use to build AI applications.
Business strategists can fold AI into their companys workflows and processes, or spearhead product development.
Domain experts understand how to apply AI to their field, while policymakers can craft AI ethics and use guidelines.
But what do you do once youve identified where you want to go?
Getting experience in AI, and developing skills in it, is a tricky proposition because the field is still so nascent. Plus, things are evolving at breakneck speed; what worked a couple years ago may not be a silver bullet today. But some strategiesbeing scrappy, curious, and adaptablecould prove timeless. We interviewed both HR and recruiting pros, as well as people who have managed to build up their AI skills to land a job in the industry, to learn:
What AI industry insiders at LinkedIn and Amazon recommend are the surefire ways to get a hiring manager’s attention
How workers are turning their regular jobs into “AI jobs” to get experience
Where one talent recruiter looks to see if someone is working on developing AI skills
1. Figure out ways to learn on the job
While companies such as Boston Consulting Group (BCG) and Thomson Reuters are rolling out company-wide initiatives to ensure their entire staff gets trained in AI, that isnt true of most companies. Only 2 in 5 employees report receiving AI training on the job.
If your company doesnt have AI training, get on projects that do involve AI.
Get some experience at your existing company before you try to jump into a truly AI-focused role, says Cheryl Yuran, chief human resources officer at Absorb Software, an AI-powered learning platform provider. Have something on your résumé to talk about from an AI standpoint.
Yuran points out that Absorb isnt able to find enough people with AI experience for all of their teams. Thats how few people are out there in the workforce with an actual background in it.
Instead, the company makes sure there are one or two members with AI experience on their teams. The remaining jobs go to candidates or insiders who demonstrate they can add value, whether its deep product knowledge or excellent communication skills.
If there arent AI projects or initiatives at your job, create them. Or experiment with ways to use AI to help you do your job.
Gabriel Harp, a former product manager for multiple companies in academic publishing, oversaw the launch of an AI-powered writing assistant in 2023 at Research Square, an Inc. 5000 company. Although my degree is in English and German, I’ve spent more than a decade building software products, Harp says.
For the AI writing assistant, Harp set the initial vision and scope of the project, working on the branding and go-to-market strategy, conducting quality analysis, and much more. Harp wasnt an engineer, yet he still leveraged his background to get great AI experience just before it was popular (or needed) to have any.
Since then, hes served as head of product strategy at a startup that uses AI to build privacy tools.
When Harp went on the job market, he had plenty to discuss during interviews, although he has a degree in the humanities. Since Id been using AI in the workplace, I was more familiar than the average person with these tools, he says.
He recently landed a senior staff product manager job at Mozilla.
Were seeing a lot of emerging talent or people who want to shift their career path, says Prashanthi Padmanabhan, VP of engineering at LinkedIn, who regularly hires for AI talent. Nothing beats showing youve actually [used AI] on the job.”
2. Take a course
If getting close to an AI project at work isnt an option, you can always take courses.
Right before the pandemic, Amanda Caswell was working as a copy lead at Amazon when she became interested in AI. She started listening to podcasts about AI and signed up for courses, including an online prompt engineering class at Arizona State University, an AI boot camp by OpenAI, and a generative AI and prompt engineering master class by LinkedIn.
Start at the 101 level, even if you have some experience, she says. That way youll know industry best practices, which can help you teach others. Because who knows? You might have to do a job in AI training.
In 2020, Caswell started getting gigs as a prompt engineer at Upwork and has made close to $200,000 on the platform, only working about 20 hours a week. In addition, her knowledge of prompt engineering helped her land a job as an AI journalist at Toms Guide.
Similarly, Cesar Sanchez, a full-stack engineer (who is now an AI engineer) became interested in AI in 2023. He immediately signed up for a Coursera course on generative AI with large language models to get an understanding of the fundamentals.
It was a great decision. It offered me a strong foundation and helped me understand the theory, Sanchez says. He also signed up for another course that offered im access to a network of AI engineers. While I didnt necessarily learn new things, I was able to connect with other engineers and compare my skills to what else was out there in the market, he adds. Plus, I got lots of free credits for using tools and platforms.
3. Take on a side project
However, even if you arent able to fold AI into the job or take a course, recruiters say theres always the trusty side project. Having a side gig is often a privilege thats unavailable to some, but having one can sometimes grow into something that’s more full-time, sustainable, and meaningful, regardless of the field.
AI, experts say, may be no different.
A lot of candidates will say, I just focus full-time on my current role, says Taylor King, CEO of Foundation Talent, which recruits for top tech startups. But the ones really thriving are the people who dive headfirst into new AI or LLM tools, constantly experimenting and building on the side,” he adds. “An active GitHub tells you theyre genuinely curioussomeone whos growing beyond the boundaries of their job, not defined by it. (A McKinsey report found that people who are adaptable are 24% more likely to be employed.)
Nico Jochnick had no background in AI, but managed to land a job as lead engineer at Anara, an AI startup that helps research teams organize and write scientific papers. He says he got a job in AI because of his experience using AI for side projects.
I was fascinated with AI and using Cursor to code side projects, and was doing hackathons, he says. [Anaras founder] and I knew these tools were giving us tons of leverage, and we connected over that.
While Harp, now at Mozilla, was job searching, he also worked on AI side projects, such as using AI coding tools to create a bingo game for his favorite podcast, as well as a recruiting tool in ChatGPT that allowed recruiters to ask questions about his work experience. I was worried about getting rusty, he says. I needed to continue experimenting with the tools out there.
4. Create your own job
Ben Christopher, a screenwriter, taught himself to code in order to keep the lights on. He started experimenting with AI in 2022 and built Speed Read AI, a tool that summarizes scripts and provides business insights, such as budget estimates, for Hollywood executives. I started showing it to some people in the industry, and got enough feedback where people said, Well pay for that, Christopher said.
Today, his team is five people strong with a growing customer base. (Christopher is careful to stress the point of Speed Read AI is to help Hollywood executives dig through massive slush piles and find more unique scripts.)
Meanwhile, Victoria Lee originally trained as a lawyer but then took a coding boot camp when she felt like she was getting pigeonholed in her career development. She graduated from the boot camp and got her first coding job in 2022, a few months before ChatGPT launched publicly. In her spare time, she had started putting publicly available legal contracts into ChatGPT for analysis and comparing them with her own. She built an understanding of what ChatGPT did well, and where it had gaps.
Lee realized the legal industry was embracing AI, and that she was perfectly positioned to fill a gap; she knew what lawyers wanted and also knew how to speak to engineers.
She landed a job in product strategy at eBrevia, which uses AI in mergers and acquisitions (M&A) due diligence. However, Lee realized she could add more value by creating her own company. Today, she provides legal services for, as well as works with, mid-market law firms to help them implement AI and craft AI policies.
Lee recommends that people who want to go into AI should identify their specialty and build knowledge to understand how it can work better with AI, or where AI currently falls short.
Jochnick has since left Anara to found his own AI-powered company, which is still in stealth mode. The people Id hire are already building projects and putting them out in the world, he says. In fact, Jochnick notes the biggest mistake you can make today when experimenting with AI is not trying. Its insane to see how much more powerful you can become in a few months. This is a really fun journey to be on. Everyone should be upskilling themselves.”
Many news outlets have reported an increaseor surgein attention-deficit/hyperactivity disorder, or ADHD, diagnoses in both children and adults. At the same time, health care providers, teachers, and school systems have reported an uptick in requests for ADHD assessments.
These reports have led some experts and parents to wonder whether ADHD is being overdiagnosed and overtreated.
As researchers who have spent our careers studying neurodevelopmental disorders like ADHD, we are concerned that fears about widespread overdiagnosis are misplaced, perhaps based on a fundamental misunderstanding of the condition.
Understanding ADHD as one end of a spectrum
Discussions about the overdiagnosis of ADHD imply that you either have it or you dont.
However, when epidemiologists ask people in the general population about their symptoms of ADHD, some have a few symptoms, some have a moderate level, and a few have lots of symptoms. But there is no clear dividing line between those who are diagnosed with ADHD and those who are not since ADHDmuch like blood pressureoccurs on a spectrum.
Treating mild ADHD is similar to treating mild high blood pressureit depends on the situation. Care can be helpful when a doctor considers the details of a persons daily life and how much the symptoms are affecting them.
Not only can ADHD symptoms be very different from person to person, but research shows that ADHD symptoms can change within an individual. For example, symptoms become more severe when the challenges of life increase.
ADHD symptoms fluctuate depending on many factors, including whether the person is at school or home, whether they have had enough sleep, if they are under a great deal of stress, or if they are taking medications or other substances. Someone who has mild ADHD may not experience many symptoms while they are on vacation and well rested, for example, but they may have impairing symptoms if they have a demanding job or school schedule and have not gotten enough sleep. These people may need treatment for ADHD in certain situations, but may do just fine without treatment in other situations.
This is similar to what is seen in conditions like high blood pressure, which can change from day to day or from month to month, depending on a persons diet, stress level, and many other factors.
Can ADHD symptoms change over time?
ADHD symptoms start in early childhood and typically are at their worst in mid-to late childhood. Thus, the average age of diagnosis is between 9 and 12 years old. This age is also the time when children are transitioning from elementary school to middle school and may also be experiencing changes in their environment that make their symptoms worse.
Classes can be more challenging beginning around fifth grade than in earlier grades. In addition, the transition to middle school typically means that children move from having all their subjects taught by one teacher in a single classroom to having to change classrooms with a different teacher for each class. These changes can exacerbate symptoms that were previously well-controlled.
Symptoms can also wax and wane throughout life. For most people, symptoms improvebut may not completely disappearafter age 25, which is also the time when the brain has typically finished developing.
Psychiatric problems that often co-occur with ADHD, such as anxiety or depression, can worsen ADHD symptoms that are already present. These conditions can also mimic ADHD symptoms, making it difficult to know which to treat. High levels of stress leading to poorer sleep, and increased demands at work or school, can also exacerbate or cause ADHD-like symptoms.
Finally, the use of some substances, such as marijuana or sedatives, can worsen, or even cause, ADHD symptoms. In addition to making symptoms worse in someone who already has an ADHD diagnosis, these factors can also push someone who has mild symptoms into full-blown ADHD, at least for a short time.
The reverse is also true: Symptoms of ADHD can be minimized or reversed in people who do not meet full diagnostic criteria once the external cause is removed.
Kids with ADHD often have overlapping symptoms with anxiety, depression, dyslexia, and more.
How prevalence is determined
Clinicians diagnose ADHD based on symptoms of inattention, hyperactivity, and impulsivity. To make an ADHD diagnosis in children, six or more symptoms in at least one of these three categories must be present. For adults, five or more symptoms are required, but they must begin in childhood. For all ages, the symptoms must cause serious problems in at least two areas of life, such as home, school, or work.
Current estimates show that the strict prevalence of ADHD is about 5% in children. In young adults, the figure drops to 3%, and it is less than 1% after age 60. Researchers use the term strict prevalence to mean the percentage of people who meet all of the criteria for ADHD based on epidemiological studies. It is an important number because it provides clinicians and scientists with an estimate on how many people are expected to have ADHD in a given group of people.
In contrast, the diagnosed prevalence is the percentage of people who have been diagnosed with ADHD based on real-world assessments by health care professionals. The diagnosed prevalence in the U.S. and Canada ranges from 7.5% to 11.1% in children under age 18. These rates are quite a bit higher than the strict prevalence of 5%.
Some researchers claim that the difference between the diagnosed prevalence and the strict prevalence means that ADHD is overdiagnosed.
We disagree. In clinical practice, the diagnostic rules allow a patient to be diagnosed with ADHD if they have most of the symptoms that cause distress, impairment, or both, even when they dont meet the full criteria. And much evidence shows that increases in the diagnostic prevalence can be attributed to diagnosing milder cases that may have been missed previously. The validity of these mild diagnoses is well-documented.
Consider children who have five inattentive symptoms and five hyperactive-impulsive symptoms. These children would not meet strict diagnostic criteria for ADHD even though they clearly have a lot of ADHD symptoms. But in clinical practice, these children would be diagnosed with ADHD if they had marked distress, disability, or both because of their symptomsin other words, if the symptoms were interfering substantially with their everyday lives.
So it makes sense that the diagnosed prevalence of ADHD is substantially higher than the strict prevalence.
Implications for patients, parents, and clinicians
People who are concerned about overdiagnosis commonly worry that people are taking medications they dont need or that they are diverting resources away from those who need it more. Other concerns are that people may experience side effects from the medications or that they may be stigmatized by a diagnosis.
Those concerns are important. However, there is strong evidence that underdiagnosis and undertreatment of ADHD lead to serious negative outcomes in school, work, mental health, and quality of life.
In other words, the risks of not treating ADHD are well-established. In contrast, the potential harms of overdiagnosis remain largely unproven.
It is important to consider how to manage the growing number of milder cases, however. Research suggests that children and adults with less severe ADHD symptoms may benefit less from medication than those with more severe symptoms.
This raises an important question: How much benefit is enough to justify treatment? These are decisions best made in conversations between clinicians, patients and caregivers.
Because ADHD symptoms can shift with age, stress, environment, and other life circumstances, treatment needs to be flexible. For some, simple adjustments like classroom seating changes, better sleep, or reduced stress may be enough. For others, medication, behavior therapy, or a combination of these interventions may be necessary. The key is a personalized approach that adapts as patients needs evolve over time.
Carol Mathews is a professor of psychiatry at the University of Florida.
Stephen V. Faraone is a distinguished professor of psychiatry at SUNY Upstate Medical University.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
When someone opens the door and enters a hospital room, wearing a stethoscope is a telltale sign that theyre a clinician. This medical device has been around for over 200 years and remains a staple in the clinic despite significant advances in medical diagnostics and technologies.
The stethoscope is a medical instrument used to listen to and amplify the internal sounds produced by the body. Physicians still use the sounds they hear through stethoscopes as initial indicators of heart or lung diseases. For example, a heart murmur or crackling lungs often signify an issue is present. Although there have been significant advances in imaging and monitoring technologies, the stethoscope remains a quick, accessible, and cost-effective tool for assessing a patients health.
Though stethoscopes remain useful today, audible symptoms of disease often appear only at later stages of illness. At that point, treatments are less likely to work and outcomes are often poor. This is especially the case for heart disease, where changes in heart sounds are not always clearly defined and may be difficult to hear.
We are scientists and engineers who are exploring ways to use heart sounds to detect disease earlier and more accurately. Our research suggests that combining stethoscopes with artificial intelligence could help doctors be less reliant on the human ear to diagnose heart disease, leading to more timely and effective treatment.
History of the stethoscope
The invention of the stethoscope is widely credited to the 19th-century French physician René Theophile Hyacinthe Laënnec. Before the stethoscope, physicians often placed their ear directly on a patients chest to listen for abnormalities in breathing and heart sounds.
In 1816, a young girl showing symptoms of heart disease sought consultation with Laënnec. Placing his ear on her chest, however, was considered socially inappropriate. Inspired by children transmitting sounds through a long wooden stick, he instead rolled a sheet of paper to listen to her heart. He was surprised by the sudden clarity of the heart sounds, and the first stethoscope was born.
Over the next couple of decades, researchers modified the shape of this early stethoscope to improve its comfort, portability, and sound transmission. This includes the addition of a thin, flat membrane called a diaphragm that vibrates and amplifies sound.
The next major breakthrough occurred in the mid-1850s, when Irish physician Arthur Leared and American physician George Philip Cammann developed stethoscopes that could transmit sounds to both ears. These binaural stethoscopes use two flexible tubes connected to separate earpieces, allowing clearer and more balanced sound by reducing outside noise.
These early models are remarkably similar to the stethoscopes medical doctors use today, with only slight modifications mainly designed for user comfort.
Listening to the heart
Medical schools continue to teach the art of auscultationthe use of sound to assess the function of the heart, lungs, and other organs. Digital models of stethoscopes, which have been commercially available since the early 2000s, offer new tools like sound amplification and recordingyet the basic principle that Laënnec introduced endures.
When listening to the heart, doctors pay close attention to the familiar lub-dub rhythm of each heartbeat. The first soundthe lubhappens when the valves between the upper and lower chambers of the heart close as it contracts and pushes blood out to the body. The second soundthe duboccurs when the valves leading out of the heart close as the heart relaxes and refills with blood.
Along with these two normal sounds, doctors also listen for unusual noisessuch as murmurs, extra beats, or clicksthat can point to problems with how blood is flowing or whether the heart valves are working properly.
Heart sounds can vary greatly depending on the type of heart disease present. Sometimes, different diseases produce the same abnormal sound. For example, a systolic murmuran extra sound between first and second heart soundsmay be heard with narrowing of either the aortic or pulmonary valve. Yet the very same murmur can also appear when the heart is structurally normal and healthy. This overlap makes it challenging to diagnose disease based solely on the presence of murmurs.
Teaching AI to hear what people cant
AI technology can identify the hidden differences in the sounds of healthy and damaged hearts and use them to diagnose disease before traditional acoustic changes like murmurs even appear. Instead of relying on the presence of extra or abnormal sounds to diagnose disease, AI can detect differences in sound that are too faint or subtle for the human ear to detect.
To build these algorithms, researchers record heart sounds using digital stethoscopes. These stethoscopes convert sound into electronic signals that can be amplified, stored, and analyzed using computers. Researchers can then label which sounds are normal or abnormal to train an algorithm to recognize patterns in the sounds it can then use to predict whether new sounds are normal or abnormal.
Researchers are developing algorithms that can analyze digitally recorded heart sounds in combination with digital stethoscopes as a low-cost, noninvasive, and accessible tool to screen for heart disease. However, a lot of these algorithms are built on datasets of moderate-to-severe heart disease. Because it is difficult to find patients at early stages of disease, prior to when symptoms begin to show, the algorithms dont have much information on what hearts in the earliest stages of disease sound like.
To bridge this gap, our team is using animal models to teach the algorithms to analyze heart sounds to find early signs of disease. After training the algorithms on these sounds, we assess their accuracy by comparing them with image scans of calcium buildup in the heart. Our research suggests that an AI-based algorithm can classify healthy heart sounds correctly over 95% of the time and can even differentiate between types of heart disease with nearly 85% accuracy. Most importantly, our algorithm is able to detect early stages of disease, before cardiac murmurs or structural changes appear.
We believe teaching AI to hear what humans cant could transform how doctors diagnose and respon to heart disease.
Valentina Dargam is a research assistant professor of biomedical engineering at Florida International University.
Joshua Hutcheson is an associate professor of biomedical engineering at Florida International University.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
When Accenture announced plans to lay off 11,000 workers who it deemed could not be reskilled for AI, the tech consulting giant framed the decision as a training issue: some people simply cannot learn what they need to learn to thrive in the world of AI. But this narrative fundamentally misunderstandsand significantly underplaysthe deeper challenge.
Doug McMillon, the CEO of Walmart, pointed to this bigger challenge recently when he said, AI is going to change literally every job. Now, if this turns out to be true, every role will have to be reimagined. And when every role changes, this is more than a change in each job or even a specific field. It implies a profound and systemic change in the nature and meaning of the work itself.
For instance, when a customer service reps job changes from answering questions to managing AI escalations, they are no longer doing old-fashioned customer servicethey are doing AI supervision in a customer service context. Their supervisor isnt managing people anymore; they are orchestrating a hybrid intelligence system composed of humans and AI. And HR isnt evaluating communication skills; they are assessing humanAI collaboration capacity. The job titles remain the same, but the actual work has become something entirely different.
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You cannot prepare people for this disruption by sending them to a three-day workshop on how to prompt more effectively. When the change is as systemic as this, the real question is not whether individuals can be separately reskilled. It is whether organizations can transform themselves at the scale and speed AI demands.
Two types of transformation
To understand the reskilling demands created by AI transformation, it helps to distinguish between bounded and unbounded transformations.
Bounded transformations are organizational changes that follow a predictable path, starting from specific areas of operation with well-defined capabilities to develop. They unfold in distinct stages, allowing companies to master one phase before moving to the next.
Unbounded transformations, on the other hand, are sweeping changes that affect all parts of an organization at the same time, with no single point of origin. Because they simultaneously alter job functions, competencies, processes, and performance measures in interconnected ways, they can’t be tackled piecemeal or rolled out sequentiallythey demand a holistic, coordinated strategy.
The AI revolution is a paradigmatic example of an unbounded transformation, as it fundamentally reshapes how we think, work, and create value across every industry, function, and level of the organizationredefining not just individual tasks but the very nature of human contribution to work itself.
And that means that it is not enough to simply reskill employees for AI. Instead, business leaders will need to transform the entire ecosystem of workthe infrastructure, the interconnected roles, and the culture that enables change. And they will often need to do all of this across the entire organization at oncenot sequentially, not department by department, but everywhere simultaneously.
There are three key dimensions that organizations need to address if they are to successfully transform themselves and reskill their workers for the AI revolution.
1. Rebuilding the infrastructure of work
Most reskilling budgets cover workshops and certifications. Almost none cover what actually determines success: rebuilding the systems people work within.
For example, AI often now handles routine inquiries in contact centers while humans tackle complex cases. As McKinsey argues, successfully implementing this shift demands far more than teaching agents to use AI tools. Businesses must rethink operating models, workflows, and talent systemscreating escalation protocols that integrate with AI triage, metrics that measure human-AI collaboration rather than individual ticket counts, and training that builds the judgment needed to handle the ambiguous cases that AI cant decide. Career paths and team structures must evolve to support hybrid human-AI capacity.
Very little of this work is training in any classical senserather, it is organizational architecture and system-building. And the organizations that do not undertake this work will find that their AI reskilling programs will inevitably fail.
2. The network effect: why roles must transform together
Organizational roles do not exist in isolation. They are interconnected nodes in an organizational network. When AI transforms one role, it also transforms every other role it touches.
For example, when AI chatbots handle routine customer inquiries, frontline agents typically shift to managing only complex situations, which may be more emotionally charged for the client. This immediately transforms the role of their trainers and coaches, who must now redesign their curriculum away from teaching efficient delivery of scripted informational responses toward teaching de-escalation techniques, empathy skills, and complex judgment calls. Further, team supervisors will now no longer be able to evaluate performance based on call handle times and throughputthey must instead develop new frameworks for assessing emotional intelligence and problem-solving under pressure.
The result is that holistic and comprehensive role redesign is essential if employees are to be successfully reskilled for AI. AI transformation requires synchronized change across interconnected roleswhen one piece of the network shifts, every connected piece must shift with it.
3. Cultural transformation
As Peter Drucker almost said, culture eats reskilling for breakfast. It is crucial for organizations to understand that cultural transformation is not a nice-to-have follow-on that comes after technical change. Rather, it is the prerequisite that determines whether technical change takes root at all. Without the right culture, training budgets become write-offs and transformtion initiatives become expensive failures.
Consider a financial services firm training analysts on AI tools. If the culture punishes AI-assisted mistakes more harshly than human mistakes, adoption dies. If success metrics still reward heroic individual effort, collaboration with AI will be undermined. If executives do not visibly use AI and acknowledge their own learning struggles, teams will treat it as optional theater rather than strategic imperative.
The culture that enables AI reskilling is one built on curiosity, not certainty. This culture prizes experimentation over perfection and treats failure as data, not disgrace. Indeed, because AI tools evolve so quickly, the defining capability of an AI-ready culture is not mastery but continuous learning. Relatedly, psychological safety becomes essential: people must feel free to test, question, and sometimes get it wrong in public.
And the signal for all of this comes from the top. When leaders openly use AI, admit what they dont know, and share their own learning process, they make exploration permissible. When they do not, fear takes its place.
In short, successful AI cultures dont celebrate competencethey celebrate learning.
Conclusion
AI reskilling is not a training challengeit is an organizational transformation imperative. Companies that recognize this will rebuild their infrastructure, redesign interconnected roles, and cultivate learning cultures. Those that dont will keep announcing layoffs and blaming workers for failures that were always about systems, not people.
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AI is often sold as the ultimate productivity hack. Just imagine: the report you dreaded writing, drafted in seconds. The spreadsheet you didnt want to touch, analyzed instantly. The code that once took you days, generated before lunch. For professionals who already struggle with overwhelm and the daily battle to manage their time, AI feels like salvation.
At Lifehack Method, where we help clients master time management and build systems for living fulfilling, balanced lives, we see this every day. People are desperate for tools that promise to take the weight off their shoulders. AI seems like the next logical step in that search. Theres no denying the dopamine hit of a blank page suddenly filling with words or lines of code. AI gives the illusion of acceleration, and in the moment, that feels like productivity. Youre doing something, and the grind of starting from scratch is gone.
But theres a problem: faster doesnt always mean more productive, and saved time doesnt always translate into better outcomes. The real test of productivity isnt how quickly you start, but whether you finish with work thats accurate, useful, and aligned with your goals. Thats where cracks begin to show.
AI can make you feel productive without actually being productive
A recent MIT study found that 95% of generative AI pilots in companies produced little to no measurable impact on profit and loss, despite $3040 billion in enterprise investment, because most GenAI systems do not retain feedback, adapt to context, or improve over time. In other words, the time people think theyre saving isnt translating into organizational productivity.
A similar story shows up among software developers in a recent controlled study. After trying AI coding assistants, developers estimated they experienced 1030% productivity gains. But in actuality, experienced coders took 19% longer when using AI tools on codebases they knew well. They not only lost time in practicethey walked away convinced theyd saved it. Thats a dangerous mismatch.
McKinseys research adds nuance: AI can indeed help with repetitive or shallow work tasks like painstakingly referencing large documents or analyzing invoices. But the productivity boost shrinks when tasks are complex or require deep, sustained attention. In other words, AI may help you clear the easy stuff off your plate, but its harder to get it to do the work that really moves the needle.
Why is that?
The 90% mirage
Heres the paradox of AI: it often gets you 90% of the way there, which feels like a huge time savings. But that last 10%checking for errors, refining details, making sure it actually workscan eat up as much time as you saved. The most common mistake is assuming 90% is good enough and shipping it.
Jeff Escalante is an engineering director at Clerk, puts it bluntly: Anything that you ask it to do, it will more than likely end up making one or more mistakes in what it puts out. Whether thats fabricating statistics, or making up things that are not real . . . or writing code that just doesnt work, he says. Its something that is really cool and really interesting to use, but also is something that you have to know you cant trust and cant rely on. It needs to be reviewed by an expert before you take what it puts out and deliver it, [especially if] its sensitive or important.
His advice? Treat AI like an intern: great for low-level work, occasionally useful when given training, but absolutely not someone youd send into a client meeting unsupervised. And if youre hoping eventually itll be foolproof, think again.
Jeff Smith, PhD is the founder of QuantumIOT and a serial technology entrepreneur. He says its important to think of the AI as an assistant because it still makes mistakes and it will make mistakes for a long time. Its probabilistic, not deterministic.
If youre a domain expert, you can spot and fix that last 10%. If youre not, you risk handing off work that looks polished but is quietly broken. That means wasted time correcting mistakesor worse, reputational damage. Many ambitious employees eager to level up with AI end up doing the opposite: walking into client pitches with beautiful decks full of hallucinated insights and an action plan that doesnt match the Statement of Work.
So should we throw AI out the window? Not exactly. But definitely stop treating it like a self-driving car and more like a stick shift: powerful, but only if you actually know how to drive.
How to use AI without losing control of your time
The most productive people dont hand over the keys to AI. They stay in the drivers seat. Here are a few rules emerging from early research and expert guidance:
Be the subject matter expert. If you dont know what excellent looks like, AI can lead you astray. The time you save drafting could vanish in endless rounds of corrections.
Use AI as a draft partner, not a finisher. The sweet spot is breaking inertiahelping you brainstorm, sketch a structure, or generate a starting point. Iterative prompting is the key to better AI outputs, but the final say will always belong to you.
Automate the shallow, protect the deep. Let AI knock out routine, low-value worksummaries, boilerplate, admin, certain emails. Guard your deep-work hours for the kind of thinking that actually moves the needle. Real productivity isnt about speed; its about aligning time with your top priorities.
Track actual outcomes. Dont confuse the feeling of speed with actual results. Measure it. Did the AI really shave an hour off your workflowor just generate more drafts to wade through?
And keep some perspective: were still in the early-adopter stage. As Smith puts it, Itll be a bit of a rocky road [but] theres tons of great tools that are going to come your way.
Productivity is still human business
At its best, AI helps remove the drudge work that crowds our days, giving us more room to think, plan, and focus on what matters. At its worst, it tricks us into mistaking busywork for progress.
AI wont manage your time for you. It wont choose your priorities or tell you which meetings to skip. That disciplineof mastering your schedule, focusing on high-leverage work, and knowing where your energy should gostill rests on human shoulders. Once that foundation is in place, AI can be a powerful ally. Without it, AI risks amplifying the chaos.
AI is a fast, powerful, occasionally unreliable tool. But like any tool, it only works if you weld it with intention. Youre still the driver. AI can help you go faster, but only if you know where you want to go.
So long, nine-to-five. There’s a new work schedule that’s taking over. The grueling “996” schedulewhich stands for 9 a.m. to 9 p.m., six days a weekis gaining momentum across the U.S., especially in certain industries.
If a 72-hour work week sounds all-consuming, that’s precisely the point. The 996 schedulewhich became popularized in China, eventually leading to protests and even claims that it led to a handful of worker deathsis meant to foster a eat-sleep-work lifestyle. Keith Spencer, a career expert at FlexJobs, told Fast Company that the trend is most commonly being seen across AI startups that “are embracing this approach to accelerate growth and remain competitive on a global scale.”
While the intense work ethic sounds overwhelming, Spencer says that some young and hungry workers may actually be drawn to it. “Certain employees, especially younger workers, may even welcome this level of intense dedication, particularly when additional pay or incentives are offered,” he explains.
That may be especially true as the rise in 996 culture has been touted by major tech leaders like Elon Musk, who have long promoted a work ethic that asks employees to make some major sacrifices. Musk opened up about the need for increased time commitments on X back in 2018 in a tweet promoting working for his companies as being revolutionary, but requiring immense dedication. “There are way easier places to work, but nobody ever changed the world on 40 hours a week,” Musk wrote.
When a commenter asked the Tesla CEO what the right number of hours a week was, he replied that it “varies per person, but about 80 sustained, peaking above 100 at times. Pain level increases exponentially above 80.
With that same hardcore work ethic in mind, companies embracing the lifestyle seem only to be interested in hiring employees who are “obsessive,” a word that appears on New York City-based AI startup Rilla’s career page to describe those who work there. Rilla explains on its applications that candidates who aren’t “excited” about working “70 hrs/week in person with some of the most ambitious people in NYC” should not apply.
Will Gao, the companys head of growth, previously told Wired about the benefits of the schedule. There’s a really strong and growing subculture of people, especially in my generationGen Zwho grew up listening to stories of Steve Jobs and Bill Gates, entrepreneurs who dedicated their lives to building life-changing companies, Gao explained. Kobe Bryant dedicated all his waking hours to basketball, and I dont think there are a lot of people saying that Kobe Bryant shouldnt have worked as hard as he did.
At Cognition, a San Francisco startup that is building an AI software engineer, the mansion workspace has living quarters for employees who don’t have time to go home. The company’s CEO Scott Wu explained what’s expected on X. “Cognition has an extreme performance culture, and were up-front about this in hiring so there are no surprises later,” Wu wrote. “We routinely are at the office through the weekend and do some of our best work late into the night. Many of us literally live where we work.”
The 996 trend seems to be taking off in the U.S. at a time when burnout is already at an all-time high. A 2025 report from online marketplace Care.com found that burnout was more impactful than employers thought. Companies believed 45% of their workers were at risk of burnout. But a staggering 69% of employees said they were actually at moderate to high risk.
For that reason, Spencer warns that companies should “exercise caution” when leaning into the 996 schedule. In addition to burnout and overwhelm, Spencer says that overworking can even trigger “a quarter-life career crisis” when employees feel disconnected with their career as a result of overworkingwhich isn’t great for the employee and doesn’t serve the company either.
Winter Peng, founder and CEO of Silveroak Capital Academy, an elite career coaching and mentorship firm, agrees that the hustle culture can backfire. She tells Fast Company that it “destroys the creativity that drives real innovation.” Peng continues: “U.S. startups adopting 996 are trading innovation for compliance” and says that ultimately, “their best talent will simply leave” in favor of companies who believe in work-life balance.
E-commerce continues to eat up ever-increasing share of the U.S. retail market: Americans bought more than $3.3 billion of items online every day in the second quarter of last year, according to U.S. Census Bureau data. Online retails share of spending is increasing with every year that passes.
Traditionally, thats meant typing a term or phrase into a search bar and clicking through to a shopping basket. But the AI revolution is poised to swamp online retail, too, with agentic AI set to shop on behalf of customers. The e-commerce sector is rapidly preparing for whats about to comean influx of non-human customers acting on behalf of humans.
We avoid hype around technology, but AI agentic shopping could bring huge changes to retail if it is widely adopted, says Clare Walsh, director of education at the Institute of Analytics, a professional body for data analytics experts.
The usually staid professional organization is full-throated in its belief that agentic AI shopping could change society. AI-empowered agentic shoppersrobots that learn your shopping needs and preferences and then shop for youhave the potential to be as disruptive for e-commerce as moving bricks and mortar retail online, Walsh says.
Those within the retail sector are equally enamored with the concept of AIs arrival. For many years now, eCommerce shopping experiences have consisted of a search bar and a long list of item responses,” Doug McMillon, CEO of Walmart, said in a statement announcing his retailers partnership with OpenAI to enable shoppers to buy things directly through ChatGPT with the aid of AI agents. That is about to change.
The early data suggests that the reality is matching the hype. AI-driven traffic to retail sites was up 4,700% in the U.S. in the last 12 months, according to Visa. The future isnt coming, its already checking out, says Rubail Birwadker, global head of growth at the credit-card company.
Shoppers want AI to help them, according to Birwadker, who points to research that 85% of shoppers say AI agents improved their experience. Separate research, provided to Fast Company by consumer insights company GWI, suggests one in five people are comfortable receiving product recommendations from AI agents. Data from consultancy Kearney indicates 60% of consumers plan to use AI agents to shop in the next year.
But ensuring those shopping interactions are secure is trickier.
Investment in cleaner data
In mid-October, Visa launched its Trusted Agent Protocol (TAP), a framework that would allow AI agents to share and access data that would ensure it can protect against fraud and bot activity. This enables merchants to avoid blocking legitimate transactions and degrading user experience, says Birwadker.
For now, TAP applies only to the Visa network. But having established it across their payments system, the massive payment processing giant intends on broadening its use. Enabling agents to safely and securely act on a consumers behalf requires an open ecosystem-wide approach and we will look to extend Trusted Agent Protocol to be compatible with other payment networks and methods in future phases, says Birwadker.
The behind-the-scenes transfer of data is where most within the e-commerce sector are rushing to catch up to what they predict is coming with the advent of agentic AI shoppers, says Robin Anderson, head of product management at Tribe Payments, a global paytech company. Were seeing investment in cleaner data, faster checkouts, stronger fraud controls and tighter integrations between systems, he says. This is because an AI agent will make a buying decision in seconds, and if theres frictiona payment fails, a price isnt clearthe sales gone. Anderson believes the arrival of AI shopping agents is going to change e-commerce in quite a fundamental way.
An agent-to-agent future
The future of shopping is agent-to-agent, agrees Bernadette Nixon, CEO of Algolia, an AI search company. The transaction will happen on the back end, she says. It won’t be a series of blue links. It won’t be a product listing page or a product detail page. It’ll be the transaction. And for that reason, it needs to be seamless. That requires accurate datawhich means public data scraping wont suffice. Just scraping brands or retailers websites doesn’t yield the necessary information to provide a good user experience, she says, because they don’t have accurate pricing. They don’t have accurate inventory.
Protocols and the companies behind them are therefore crucial. Visa is far from the only company in the space: online payments company Stripe has its own Agentic Commerce Protocol, an open standard developed in conjunction with OpenAI.
It all opens up new opportunities for businesses, says Daniel Ruhman, CEO and co-founder of Brazilian fintech Cumbuca, where early AI agent adoption has run ahead of other countries.
You could ask ChatGPT or Claude to find me a handbag, navigate checkout pages, and have your agent handle the payment for you, all with your consent, he says. Thats standard, but agentic AI could go further. Through this, agents can even access your financial data to offer spending insights or advice, he says, what we call agentic open finance, where an AI agent connects to your bank accountwith your permissionto help you understand and manage your money.
The Federal Reserves influence on the economy is immense, and often misunderstood. President of the San Francisco Fed Mary Daly gives an exclusive, firsthand look into the central banks daily decision-making, explaining how the Feds policies, at both the regional and national level, ripple through society. From housing prices to immigrations impact on labor, Daly weighs the major factors shaping the U.S. economy. As political and market pressures mount, she reflects on what it means to lead with discipline and data, and what every business leader can learn from the Feds balancing act.
This is an abridged transcript of an interview from Rapid Response, hosted by the former editor-in-chief of Fast Company Bob Safian. From the team behind the Masters of Scale podcast, Rapid Response features candid conversations with todays top business leaders navigating real-time challenges. Subscribe to Rapid Response wherever you get your podcasts to ensure you never miss an episode.
You run one of the Fed’s 12 regional banks. Your district covers nine Western states, plus Guam, American Samoa, The Mariana Islands. Can you briefly describe the role of your office, and how it relates to the Fed overall? When we hear Fed Chair Jerome Powell announcing a change in interest rates, are you feeding into that? How does all this work?
In 1913, when the Fed was formed, there was a decision that we shouldn’t be Washington-centered. That having a presence in Washington with the Board of Governors was important, but having 12 regional reserve banks was equally important so that we could balance out the decisions about the economy across the country, not just in DC.
So I lead one of the 12 reserve banks, and those reserve banks do feed into monetary policy. We go to each and every FOMC meeting. We are rotating on votes, but we’re always participating. We’re thinking about how our districts with the lived experience in the economy is and how that matters when we make monetary policy.
Monetary policy, the misnomer is it’s all about numbers and markets, but it’s actually about people and lived economy experiences throughout the nation. And so that’s the role of reserve banks, in addition to managing all the operational duties that our teams have, including making sure you have cash when you need it, that your bank can get it and distribute it, making sure the banking system is safe and sound.
You’ve said there’s no politics in the Fed. You’re not funded by the federal government, so a shutdown doesn’t affect you, but everybody tries to influence you guys, policymakers, the White House, investors. How do you keep that politics and that pressure out of what you do?
The founding of the Federal Reserve 1913 had two elements to it that I think have been durable over time and led the way for central banking across the globe. First was that you had to have a regional voice and the second was that you had to be independent. Because monetary policy is made for the longer run and the decisions we make on where to put cash depots and how to distribute our supervision, that’s all got to be done no matter what administration is in place.
So to be durable, especially on the monetary policy part, Congress said let’s make these individuals independent of political persuasion and really thinking about the goals we gave them, and in our case, it’s price stability and full employment, making sure inflation is at 2%, making sure that the economy is not producing lots of unemployment or running so hot, so un-sustainably that inflation should go up. So those are the goals we have.
You asked how do you maintain that? How do you not get influenced? Ultimately, who we work for is the American people. Of course, individuals have points of view and we have to consider those because otherwise we’d just be in an echo chamber. But there’s a difference in listening to understand and listening to be persuaded.
And when the President tries to remove a Fed Governor, as President Trump has done with Lisa Cook, how distracting is that from
It’s really not distracting from the task at hand. Let me just speak about myself. We’re fiduciary stewards of public trust and public responsibilities, and so that’s where I have to attend. Now, I think about not just what’s right in front of me, but ensuring that the American people have a stable and healthy economy over the long term and that the independence of the Fed is preserved not just for the next two months or two weeks, but in fact over the time period, passing that baton to the next generation of leaders.
There’s been so much disruption this year in 2025. Are there particular economic indicators that you are most focused on right now?
So I think about it as a three-legged stool. So the first component is the public data, the things we get from the government, the things that we get on a regular basis. They’re very, very important, but they’re only one part of our overall data collection. We also get data from the private sector. One of the more critical components of that three-legged stool, which is underappreciated in my opinion, is the time that the reserve bank Presidents in particular spend talking to people, to CEOs, small, medium and large businesses, to community members, civic leaders, unions and workers thinking about not just what were they doing last week, but what are they doing going forward.
So right now, I’m very focused on that third leg. And the reason is because when you get to a point where the economy is changing, you have to rely on people who are telling you not what they were doing last week, but what they are doing next week, the next month, the next quarter and ultimately, the next year. And we take that valuable information back to the FOMC meeting. It’s really a robust process and one that I think is critical at these moments.
Obviously, the economy is always changing to some extent, but it certainly feels like we’re at a certain kind of inflection point. I know you’ve rated the sentiment of your region as cautiously optimistic, which is a little incongruous with an economy that seems like it’s moving to something we’re not quite sure where it’s going to go. Can you address that disconnect and maybe explain how and where you see the economy moving?
Absolutely. So there is quite a bit of uncertainty still, not as high as earlier in the year. The uncertainty really spiked after April 2nd, after Liberation Day. There was just so much uncertainty people didn’t know if they were going to be able to buy their smartphone or if they should buy it right away or if they should wait.
Consumers were uncertain. Businesses were uncertain about what’s this going to mean. But at this point, I think those things have settled, and the economy weathered that fairly well. The unemployment rate has gone up a little bit, but not that much. Inflation has gradually come down except in the tariffed sectors.
So the only places where you see prices rising are in the ones direcly affected by tariffs. And so people think of that increasingly as a one time price level adjustment and then they’ll be okay.
Another thing that I think is important is pick a basket of goods that you like to purchase. Put them in a cart at one of your favorite online retailers and then check what has happened to that basket of goods over time. And what you’re seeing is that while certain items have gone up, other ones are being deeply discounted, so people feel like they’re not losing the kind of ground they lost in the big inflation rise after the pandemic. So I think that gives people some confidence.
Recession indicators were quite high and rising earlier in the year and now they’re not really predicting it at all. Consumer sentiment has gone back up after falling, business sentiment has gone back up after falling. So I think that’s where I get the cautious optimism. I was at University of Utah a couple weeks ago and the students are optimistic, and I was really encouraged by that because that generation is like a bellwether. They see that if they learn these new skills, AI, et cetera, they can really make a dent in the economy.
So what is the new economy going to look like? The truth is no one knows, but we do know what the elements will be. Certainly, artificial intelligence is making its way. Is it going to be transformative? Is this going to be the new steam engine or electricity? I don’t know. But it is making a contribution to people’s ability to do things faster, better, cheaper and hopefully, will also make a contribution to us doing things that we never imagined were possible.
