If you don’t want to be left behind by the AI revolution, you really need to start paying for it.
At least thats become the common refrain among some AI enthusiasts, who seem intent on instilling FOMO in less technical users. The free versions of ChatGPT and Claude, they say, are woefully inadequate if you want to understand where things are headedso stop being a cheapskate and hand over your $20 (or $200) a month like the rest of us.
“Judging AI based on free-tier ChatGPT is like evaluating the state of smartphones by using a flip phone,” HyperWrite CEO Matt Shumer recently wrote in a widely shared essay on AI’s impact. “The people paying for the best tools, and actually using them daily for real work, know what’s coming.”
I’m giving you permission to safely ignore this advice, and to not feel bad about it. While an AI subscription might make sense if you’re running into specific frustrations with the free versions, you can still get plenty of mileage without paying, and learn a lot about the state of AI in the process. Don’t be frightened into buying something that hasn’t actually proven its value to you.
The state of the art is still free
One way that AI boosters try to scare you into paying for AI is by arguing that the free versions are already obsolete, so any negative impressions you might’ve gotten from them are misguided.
“Part of the problem is that most people are using the free version of AI tools,” Shumer wrote in his essay. “The free version is over a year behind what paying users have access to.”
This claim is provably false:
The free version of ChatGPT includes access to GPT-5.2, OpenAI’s latest model, which launched in December.
The free version of Google Gemini includes access to Gemini Pro 3.1, which launched on February 19.
Claude’s free version doesn’t include Opus 4.6, but has the same Sonnet 4.6 model that the paid version uses by default. It launched on February 17.
Microsoft 365 subscribers can also select “Smart Plus” in Copilot to use GPT-5.2, without a premium AI subscription.
xAI’s Grok 4 is available for free.
Of course, the free versions of these tools all have usage limits, but so do the paid ones. When I signed up for a month of Claude Pro to test Opus 4.6, I quickly ran into yet another paywall. To continue the conversation, I had to either buy pay-as-you-go credits or upgrade to the $200-a-month Claude Max plan. Without paying more, I couldnt use Claude at allnot even Sonnet 4.5until my limit reset. My main takeaway was that I should have just stuck with Sonnet in the first place.
Instead of paying for some vague feeling that you’re getting the state of the art, you should play around with what AI companies offer for free. Make them demonstrate that the results are meaningfully different before you consider paying them, not after.
AI should prove itself to you, not vice versa
For AI boosters, the corollary to paying for AI is that you also need to throw immense amount of time into figuring out what it’s for. Ethan Mollick, for instance, writes that you should “resign yourself to paying the $20 (the free versions are demos, not tools),” then spend the next hour testing it on various real-world tasks.
Sorry, but this is backward from how software as a service should work. It’s not your job to invest time and money into convincing yourself that AI is worth more time and money. Let the AI companies do the convincing, and don’t fall prey to FOMO in the meantime.
Playing the field is just as instructive
If you do commit to paying for an AI tool, chances are you won’t use other AI tools as much, or at all. But that in itself isn’t a great way to understand the state of AI.
What you should be doing instead is bouncing around, taking full advantage of what each AI company offers for free. That way, you’ll get a sense not just of the subtle differences between large language models, but also the unique features that each AI tool offers. You’ll also be less likely to run into usage limits, the only trade-off being that your past conversations will be scattered across a few different services.
Such behavior is, of course, wildly unprofitable for all the companies involved. But again, that’s not your problem. If you’re getting sufficient value out of free AI tools, the AI companies will have to tweak their free offerings accordingly (for instance, with ads) or come up with new features worth paying for. Claude Code, for instance, is available only with a subscription, and over time we may see more paywalled tools (like Claude Cowork, which is still in early development) that cater to specific tasks or verticals.
Until that happens, enjoy the free versions of AI tools, and rest easy knowing that you’re not missing much.
Its the last week of Black History Month (BHM) and its clear Americans are over performative values. Trite BHM-inspired merchandise sits on retailer shelves untouched while media is abuzz covering the artistry, activism, and symbolism of Bad Bunnys Super Bowl halftime show. The signal is clear: consumers are looking to brands for real solutions to real problems, not products that commodify culture.
Most companies build everything from advertising to AI for the “average user,” but in doing so, they react to rather than lead markets. Strategic leaders look to growth audiencesunderserved groups who are the fastest-growing demographicsas lead users. They are the “canaries in the coal mine” because they navigate the highest levels of systemic friction, making them the first to experience “average” design failures.
What does championing these lead users look like at a communications, product, or systems level? It looks like Elijah McCoy automating engine lubricationan innovation bred from the friction between his engineering degree and the menial labor he was forced to perform, thus creating the real McCoy quality standard. It looks like Jerry Lawson changing the economics of the gaming industry by inventing the video game cartridge that divorced its hardware from its software. And it looks like emergency medicine becoming a global standard after being piloted by the Pittsburgh Freedom House Ambulance Service who, in the face of medical bias and systemic unemployment, also redefined emergency care as a public right.
Drawing from their lived experiences in underserved groups, these pioneers didnt just solve problems; they mastered environmental friction. Today, that friction also manifests in algorithms. Championing growth audiences as lead users means ensuring they are critical AI system “stress testers.” When we fail to design for them, we allow AI data, development, and deployment to default to obtuse “averages” that can frustrate or drive away valuable customers. Three recent examples highlight issues and opportunities.
Relying on ‘Data Infallibility’ versus Lived Realities
In this Infallibility Loop bias, a brands AI trusts a data sourcelike a flawed GPS coordinate or outdated government mapas an absolute truth, even when customers provide contrary evidence. This is a digital echo of historical redlining: a systemic refusal to see humans over faulty data.
The Experience: A Black homeowner in an affluent area is penalized by an AI that confuses her address with a property in a different town, automatically forcing unnecessary flood insurance onto her mortgage and increasing the payments. Despite providing human-verified deeds and highlighting known GPS errors, the AI blocks her incomplete payments and triggers automated credit hits. A resolution only came months later after the consumer filed state-level servicer complaints.
The Fix: Prioritize Dynamic Qualitative Data Collection. Design should allow real-time, contextual evidence to override static, biased datasets. True brand innovation requires systems to yield to the experts: their customers.
Leveraging ‘Data Intimacy’ while Neglecting Situational Accuracy
This trust paradox occurs when brands use private data, but fail to combine situational data, making personalization feel like needless surveillance.
The Experience: During Januarys recent record-breaking New York snowstorm, a customer called a national pharmacys location in her neighborhood to make sure they were open. The AI-powered interactive voice response (IVR) recognized her number, asked for her birthdate, and greeted her by name. Yet, after performing this exchange, it provided a “default” confirmation that the store was open when asked. Without a car, the customer braved life-threatening conditions on foot only to find a handwritten note on the door indicating it had closed due to the storm.
The Fix: Add Good Friction. A term coined by MIT professor Renee Richardson Gosline, “Good Friction” requires that when external context (like a Level 5 storm) conflicts with standard scripts, the system pauses and verifies first.
Prioritizing ‘Recency’ But Erasing Loyalty
Recency bias in algorithms weights the last data point more heavily potentially resulting in algorithmic erasure.
The Experience: A 20-year elite status customer calls an airline, only to be greeted by the name of his niece (a nonmember relative for whom he recently booked a one-off ticket) and then is erroneously deprioritized in the automated journey as a nonmember. In many “growth audience” and immigrant households, economics are multigenerational and communal, with a single “lead user” facilitating purchases for extended family. This airline systems “memory” was shallow, seeing only the most recent transaction and ignoring a decades-long relationship because a reservation shared the same contact number.
The Fix: Focus on Holistic Design. AI must be weighted to recognize the arc of the customer journey, ensuring that loyalty isnt erased by a single data point or the nuances of communal purchasing.
To be sure, bad data is a universal problem, but the lack of situational intelligence in our AI systems hits growth audienceslike Black consumersfirst and hardest. Because these audiences represent a disproportionate share of future consumption and have the most “cultural common denominators,” their frictions are diagnostics for markets writ large. We arent just solving for a niche by championing them as lead users, we are adopting more rigorous, empathetic, expansive, and effective standards that solve real problems for all people.
At hundreds of Burger King restaurants across the U.S., theres a new invisible worker whos tracking which ingredients are in stock, analyzing daily sales data, and checking in on whether employees are saying Thank you and Youre welcome. Its an AI assistant named Patty.
According to Thibault Roux, Burger Kings chief digital officer, the voice-activated chatbot is designed to help employees and managers handle tasks that might usually require pulling out a computer or consulting with an instruction guide. Patty began showing up at select locations about a year ago, and is now in a pilot phase at approximately 500 Burger Kings. Its expected to roll out to the rest of the chains U.S. locations by the end of the year.
On a day-to-day basis, Patty has an array of functions, from letting a manager know if a store is low on onions to helping an employee build a new burger. But it has another role thats raising quite a few eyebrows: analyzing Burger King locations based on friendliness by tracking employees use of key phrases like Welcome to Burger King, Please, and Thank you.
Online, commenters are concerned that this functionality is a slippery slope toward 1984-style employee surveillance. In an interview with Fast Company, though, Roux clarified that Patty is not being used to analyze individual employees performance, and is instead imagined as a kind of coach.
It’s truly meant to be a coaching and operational tool to really help our restaurants manage complexities and stay focused on a great guest experience, Roux says. Guests want our service to be more friendly, and that’s ultimately what we’re trying to achieve here.
Patty, are we running low on Diet Coke?
Technically, Patty is the chatbot version of Burger Kings assistant platform, which collects data from operations including drive-through conversations, inventory, and sales, and then uses AI to analyze patterns in that data. For now, Patty operates on a customized model from OpenAI, though Roux says the technology is flexible enough that it could integrate with another partner in the future (like Anthropic or Gemini) depending on the companys needs.
For managers and employees in stores, Roux says Patty operates similarly to something like Siri. Patty is activated by a small button on the side of an employees headset, and they can ask it direct verbal questions related to their specific storelike recent sales figures or inventory updatesas well as more general company information, to which the bot will provide a verbal answer.
If you’re looking to clean the shake machine [you can ask Patty] the procedures to clean it, Roux explains. Or we have a lot of limited-time offers, and sometimes they can be cumbersome to remember. You can easily tap into Patty and be like, Hey, remind me, does the new build maple bourbon barbecue have crispy jalapeos?
Patty can also reach out to employees directly if it notices a pattern of interest. For example, if Patty thinks a specific store is out of lettuce, it might ping a manager to confirm. Once its received confirmation, it can mark lettuce as sold out on that locations app and websitea process that previously would have required human intervention. Roux says franchisees and regional managers can decide how they want Patty to reach employees with information, whether its through their headsets or via a text message (though the tech is programmed explicitly to never interrupt a worker during a customer interaction).
Insights from Burger Kings Assistant platform also live outside of employees headsets. Managers can check information from the tool on an accompanying website or app. For example, Roux says, when a district manager is visiting a new store, they might ask Patty on the app, What are the top three guest complaints at this location this week? or What are their top missing items?
In an interview with Fast Company writer Jeff Beer earlier this month, Burger King President Tom Curtis said the assistant platform has already led to some significant menu changes. Curtis explained that the AI tracked all the times that team members said Im sorry, we dont have that and linked them back to a common denominator: apple pie. In January, Burger King brought back its apple pie for the first time since 2020.
Were in the idiocracy version of 1984
Pattys more straightforward uses, like helping managers access sales data and check inventory, seem fairly predictable in the context of fast food. Where Burger King is really pushing Pattys use cases, though, is with its friendliness metric.
In an interview with The Verge on February 26, Roux said Patty would recognize phrases like Welcome to Burger King, Please, and Thank you, and then give managers access to data on their locations friendliness performance based on those keywords. Mere hours after that piece went live, a thread in the subReddit r/technology on Patty had already amassed more than 15,000 upvotes and nearly 3,000 comments. Common refrains from users include comparing the technology to the surveillance state in George Orwells novel 1984, labeling it authoritarian and dystopian, and accusing Burger King of employee surveillance.
“This would be criticized as being cartoonishly unrealistic in a sci-fi movie 10 years ago,” one user wrote. Another added, “We’re in the idiocracy version of 1984.”
When asked about this response, Roux says the data from employees conversations is anonymized, and that none of these friendliness metrics will be used for grading or assessing individuals. Further, he adds, Patty will not directly instruct employees on what to say or how to say it. Instead, data on friendliness will be shared with managers, who can use it for face-to-face coaching with their teams.
Still, its unclear exactly how Patty is quantifying friendliness. In a video explanation of the feature, a manager is shown asking the bot, Is there anything that needs my immediate attention? to which it responds, The teams friendliness scores this morning were the highest this week.
In an email to Fast Company, a Burger King spokesperson said, In select pilot locations, weve explored using aggregated keywords, including common hospitality phrases, as one of several signals to help managers understand overall service patterns. The tool is not used to score individuals or enforce scripts. Burger King did not respond to ast Companys request for clarification on how friendliness scores are calculated.
So far, Roux says hes seen growing interest in Patty from franchisees, with several managers making specific requests for future add-ons.
A lot of our franchisees . . . and regional general managers are very competitive, so they want to know, Hey, how do I compare to other restaurants? Roux says. I think that’s something that we’re going to be rolling out. In fact, we were looking at some of the designs earlier this week with the franchisees. So this is only the beginning.
Recently, Grok AI faced criticism after users found it was creating explicit images of real people, including women and children. Although xAI has now implemented some restrictions, this incident revealed a serious weakness. Without safeguards and diverse perspectives, girls and women are put at greater risk. The dangers artificial intelligence poses to women and girls are real and happening now, affecting their mental health, safety, healthcare, and economic opportunities.
Last fall, a mother discovered why her teenage daughter’s mental health had been deteriorating: It was a result of conversations with a Character.AI chatbot. She’s not alone. Aura’s State of Youth Report, released in December, found that parents believe technology has a more negative effect on girls’ emotions, including stress, jealousy, and loneliness51% compared with 36% for boys. Thats unacceptable, and we need to do better.
The risks extend beyond mental health. OpenAI recently reported that more than 40 million Americans seek health information on ChatGPT daily. As AI in healthcare expands, the consequences of biased training data can be dangerous. AI models that are trained predominantly on male health data produce worse outcomes for women. For instance, an AI model designed to detect liver disease from blood tests missed 44% of cases in women, compared with 23% in men.
Uneven playing field
In the workplace, AI is not leveling the playing field. Despite laws prohibiting discrimination, AI-powered hiring tools have repeatedly caused concerns about bias, fairness, and data privacy. A study published by the University of Washington found that in AI resume screenings, the technology favored female-associated names in only 11% of cases.
These failures reflect who is building our technology. Women make up just 22% of the AI workforce. When systems are designed without women’s perspectives, they replicate existing inequities and introduce new risks. The pattern is clear. AI is failing girls and women.
Pivotal moment
This could not come at a more pivotal moment in the job market. A quarter of the roles on LinkedIns latest list of the 25 fastest-growing jobs in the United States are tech-related, with AI engineers at the top. Decisions about how AI is designed today will shape access to jobs, healthcare, education, and civic life for decades. It is critical that women play an active role in developing new AI tools so that inequity is not baked into the systems that increasingly govern our lives.
Young women are not disengaged with AI. Research conducted last year by Girls Who Code, in partnership with UCLA, found that young women are deeply thoughtful about the dual nature of technology. They see its potential to advance healthcare, expand educational access, and address climate change. They are also aware of its dangers, such as bias, surveillance, and exclusion from development. This isnt blind optimism. Instead, it offers a perspective that is often missing in todays AI development.
Creating technology is an exercise of power and holds great responsibility. Since girls are often the most affected by AIs failures, they must be empowered to help lead the solutions. Women like Girls Who Code alumna Trisha Prabhu, who developed ReThink, an anti-bullying tool, exemplify this. Latanya Sweeney, recognized as one of the top thinkers in AI, founded Harvards Public Interest Tech Lab. Their achievements demonstrate the potential when women lead in tech development.
Smart steps
If we want safer, more responsible AI systems, three steps are essential.
First, computer science education should integrate social impact. Coding cannot be taught in isolation from its consequences. Students should learn technical skills alongside critical analysis of how technology shapes communities and lives. This approach produces results. For instance, one Girls Who Code student utilized the skills she learned to create an app called AIFinTech to help immigrant families manage their personal finances.
Second, women must be represented in AI development and governance, particularly those from historically underserved communities. They need seats at the tables where AI systems are designed, tested, and regulated. This means ensuring gender diversity on AI ethics boards and that government AI committees are representative of the demographics most affected.
Finally, how we evaluate artificial intelligence needs to evolve. Today, AI is assessed by efficiency, accuracy, and profitability. We must also evaluate health, equity, and well-being, especially for girls and young women. Before an AI system is deployed in a high-stakes environment such as healthcare, it should be required to pass tests for gender bias and demonstrate that it does not produce disparate outcomes. New York City, for example, requires employers that use automated employment decision tools to undergo an independent bias audit annually.
We do not have to accept AIs flaws by default. We are witnessing AIs impact on girls in real time, and we must seize the opportunity to change course while the technology is still being shaped. When girls are given the chance to lead in AI, they will build safer systems not just for themselves, but for everyone.
What began as a race to build better AI models has escalated into a competition for compute, talent, and control. Foundation modelslarge-scale systems trained on vast datasets to generate text, images, code, and decisionsnow underpin everything from enterprise software and cloud infrastructure to national digital strategies.
The industrys language around AI has grown more ambitiousand more elastic. Agentic AI has leapt from research papers to Davos billboards, while artificial general intelligence, or AGI, now appears routinely in investor decks and earnings calls. Definitions have begun to blur. Some companies quietly lower the bar for what qualifies as general, stretching the term to encompass incremental productivity gains.
Yet the economic results, particularly measurable returns on AI investment, remain uneven. According to PwCs 2026 Global CEO Survey, 56% of 4,454 CEOs across 95 countries reported neither increased revenue nor reduced costs from AI over the past 12 months. Only 12% achieved both. Even so, 51% plan to continue investing, despite declining confidence in revenue growth. The result is a widening gap between engineering reality, commercial storytelling, and public expectation.
Few voices carry as much authorityor have shaped modern AI as directlyas Andrew Ng. The founder of DeepLearning.AI and Coursera, executive chairman of Landing AI, and founding lead of the Google Brain team, Ng has helped define nearly every major phase of the field, from early deep-learning breakthroughs to the current wave of enterprise deployment. He has authored or coauthored more than 200 papers and previously led the Stanford AI Lab. In 2024, he popularized the term agentic AI, arguing that multistep, tool-using systems capable of executing workflows may deliver more near-term economic value than simply scaling larger models.
In an exclusive conversation, Ng offered Fast Company a reality check. He says true AGIthat is, AI capable of performing the full breadth of human intellectual tasksremains decades away. The true competitive frontier, meanwhile, lies elsewhere.
This conversation has been edited for length and clarity.
You helped popularize the term agentic AI to describe a spectrum of autonomy in AI systems. How did you come up with it, and how has the concept evolved as multi-agent systems move into enterprise production?
I began using the term almost two and a half years ago, though I didnt publicly take credit for it at the time. I started using it because I felt the community needed language that shifted the focus toward AI systems capable of taking multiple steps of reasoning and actionnot just a single prompt-and-response exchange. More specifically, I felt there would be a spectrum of AI systemssome slightly autonomous or slightly agentic, and others highly agenticwhere they take many steps of actions and work for a long time.
No one was using the term agentic to describe this concept before I began using it. I started introducing it in my newsletter and in talks at conferences and industry events, and it quickly gained traction there. I didnt expect marketers to run with it the way they did.
When I attended Davos this year, I saw the word plastered on the sides of buildings. Even outside San Francisco, agentic now appears on billboards. I did want to intentionally promote the use of the term, but seeing how common it has become, I sometimes wonder if I overdid it.
Enterprise adoption of agentic AI is accelerating, yet many organizations are struggling with integration, governance, and measurable ROI. Why is it so?
Two years ago, there was intense hype around AIs risks and dangers, among other concerns. Last year, businesses began shifting their focus toward real-world implementation. This year, the conversation has moved firmly to ROI. Even though many companies are not yet seeing strong returns, they continue to invest because they understand that AI will eventually deliver value. The discussion has shifted from excitement about what AI might do to a more grounded focus on how it can generate real economic impact.
Theres also an interesting split-screen dynamic emerging. On one hand, many businesses say agentic AI is not yet delivering meaningful ROI, and theyre right. At the same time, teams building agentic workflows are seeing rapid growth and real, valuable implementations. The agentic movement still has very low penetration, but it is compounding quickly.
What are the most significant mistakes enterprises make when deploying agentic systems at scale, and how should leaders rethink their technology and operating models to overcome them?
Many businesses are pursuing bottom-up innovation, which is valuable, but the limitation is that it often leads to point solutions that deliver incremental efficiency gains rather than transformative change. If AI automates just one step in a process, for example, it might save an hour of human work and reduce costs. Thats useful and worth doing, but it doesnt fundamentally change the business. Much of todays AI deployment falls into this categoryincremental improvement rather than full transformation. To unlock real value, companies need to look beyond optimizing individual tasks and start reimagining entire workflows.
Doing so requires top-down leadership. Often no single person working on one step has the authority to reshape the entire process, which is why executive-level direction becomes essential. Real impact comes from tailoring AI strategy to each organizations specific context rather than following generic industry playbooks.
There is a growing debate about whether we are in the midst of an AI bubble or simply an early infrastructure build-out comparable to the internet era. How do you distinguish between speculative hype and genuinely durable AI value being created today?
At the application layer, I dont think were in an AI bubble. AI is expanding rapidly across business use caseshow we process legal and technical documents, manage customer success workflows, conduct research, and much more. I would like to see more investment in AI applications and inference infrastructure. Right now, there simply isnt enough inference capacity, and worries around rate limits exist.
The more interesting question about a potential bubble sits in the model training layer, where infrastructure spending continues to surge. If any risk exists, its highest there because the largest investments are concentrated among a small number of players. When companies build highly specialized hardware that can only be reused for inference with some inefficiency, the risk of overbuilding increases. I dont think were overbuilding right now, but if any part of the AI market faces that possibility, its the training layer.
As the industry moves beyond a single-model mindset toward more diverse agentic systems, how should enterprises think about AI architecture? Is there likely to be one dominant framework for building scalable, real-world AI systemsor will organizations need a more flexible approach?
Software can range from five lines o code to massive systems that run for years. Because of that range, there wont be a one-size-fits-all approach to building or governing these systems. Just as we dont use a single framework to manage everything from simple scripts to enterprise platforms, we wont rely on one architecture for agentic AI. Human work itself is incredibly diversefrom basic tasks like spell-checking to analyzing complex financial documents. Since the work varies so much, the AI systems we build will also need to vary.
One principle my teams follow when building agentic AI systems is speed, as continuous improvement is essential. Our typical cycle involves building carefully to avoid major risks, testing with users, gathering feedback, and refining the system until it truly works well. That rapid loop is what helps teams build reliable, high-performing systems faster.
Agentic AI is rapidly increasing systems ability to reason and act with limited human intervention. Does the rise of agentic architectures meaningfully accelerate the path toward AGI, or are we still far from true general intelligence?
Most of the public thinks of AGI as AI that is as intelligent as people, and one useful definition is AI that can perform any intellectual task a human can. You and I could learn to fly an airplane with maybe 20 hours of training, learn to drive a truck through a forest, or spend a few years writing a PhD thesis. Most humans can do these things. Were still very far from AI meeting that definition of AGI.
For alternative definitions that some businesses have put forwarddefinitions that dramatically lower the baryou could argue we already achieved AGI. Theres a good chance that under these lower-bar definitions, some businesses will soon try to declare success. But that wont mean AI has reached human-level intelligenceit will simply mean the definition has been reworked to fit a much lower threshold.
Maybe a year ago, AGI felt 50 years away. Over the past year, perhaps weve made a solid 2% of progress, with another 49 years to go. These numbers are metaphorical, so dont take them too seriously. [Laughs] But we are closer than before, yet many decades away from an AI that matches human intelligence. If you stick with the original definitionaligned with what people genuinely imagine AGI to bewe remain very, very far away.
Is geopolitical fragmentation reshaping global AI strategy for both governments and enterprises?
One of the other big themes Im seeing is sovereign AI. The world is becoming more fragmented, and theres a lot of discussion about how nation-states want to make sure they have access to AI without needing to rely on other nations or any single company that they may not fully trust or be able to rely on in the long term. Governments and regions are thinking carefully about how to build and maintain their own AI capabilities so they can remain competitive and secure.
As AI becomes more central to economic growth and national security, this question of who controls the infrastructure and models becomes much more important. So alongside enterprise adoption, theres also a growing geopolitical dimension to AI deployment.
In 2026, as enterprises search for real economic returns from AI, what leadership decisions and workforce shifts will ultimately determine whether organizations capture meaningful value from agentic systems?
Leadership matters. When I work with CEOs, I see decisive moments when the C-suite must think strategically about what to invest in and then place those bets thoughtfully, guided by a clear understanding of what the technology can and cannot donot just the surrounding hype. In periods of transformation, leadership decisions determine whether an organization captures real value from AI or merely experiments at the margins.
I often speak with CEOs before they set a major strategic direction. No one knows exactly where AI will be in a few years, so we are operating in a kind of fog of war. But uncertainty does not mean we dont know anything. Teams and partners who understand the technology well can narrow that uncertainty significantly and make far more informed decisions.
At the same time, everyone should learn to codeor at least learn to build software with AI. AI has lowered the barrier to creating custom tools. Today my marketers, recruiters, HR professionals, and financial analysts who use AI to write code are already more productive than those who do not. When I hire, I increasingly prefer people who know how to build with AI assistance. I may have been early on this shift, but I now see more startups and established companies moving in the same direction.
Just as it became unthinkable to hire someone who could not search the web or use email, I am already at the point where I hesitate to hire knowledge workers who cannot use AI to build or automate with code.
It’s sometime in the future, and Elon Musk, Jeff Bezos, and Sam Altman have joined forces on a new venture called Energym. The global chain of gyms is designed to harness the energy of the unemployed as they exercise on machines. The generated electricity feeds the AI servers that put them out of a job. Think Planet Fitness meets the Matrix, but without living in a simulation.
Energyms mission is to feed the AI machines with human sweat, and it’s a great business model. By 2030, almost 80% of people have lost their jobs. If you have no money and no purpose, you may as well use all your free time to work out and feed AI server fans with some kilowatts. It solves our need for energy and your need for purpose, Altman says in a promotional video.
Energym, as you probably already know, is not real. But it very well could be. In this era, where so many brands and startups are constantly trying to flip the most inane ideas into the Next Big Thing to get a $50 billion valuation and an IPO, this absurd premise makes total sense.
The mockumentary-style ad fpr Energym that has been circulating on the internet captures the current AI startup circle jerk better than any I’ve seen online so far.
https://www.instagram.com/reels/DVLE-QJEf0n
The advertisement was created by Hans Buyse and Jan De Loore. The latterwho wrote the copy for the video, as well as edited and produced itis the cofounder of a one-man AI creative studio in Belgium called Kitchhock. The company has been creating all types of videos since 2011, back when there was no Seedance or Veo. But now, De Loore is using his creative chops and the latest generative video AI tech to make real ads for real companies in Belgium through his AI video studio arm, AiCandy.
Energym is just a satirical ad designed to promote his own business and destroy the very core of those who make the technology that powers his business. (Incidentally, Energym is the same name as a company that makes a very real $2,800 static bicycle designed for exercise and to produce electricity, but its not related to AiCandy’s fake ad.)
The Energym commercial is obviously tongue in cheek, as are many other videos we have seen in recent months that make fun of our increasing dependency on artificial intelligence and its power. But this one hits particularly hard. For some, it may be the Black Mirror-esque nature of it. (Theres an actual episode of the British TV series that feels like an extended version of the ad.)
Personally, it connects with the WTF-ness that the current AI situation is provoking in me on different levels. The fear of whats next. The dread of seeing reality destroyed. The disgust for the fat cats that are running this charade with no checks and nobodys permission. I find it hard to pinpoint what it is. Its just an absurd exaggeration with no logical basis that hits too close for comfortand, at the same time, makes me happy.
As a young child, interior designer Jeremiah Brent and his mother visited open houses and model homes in his hometown of Modesto, California, as a form of daydreaming. Brent walked through the houses, imagining the people who might live there, building a fantasy around what these homes could be. Since then, Brent has turned his childhood design obsession into a sprawling career: He runs a 50-person design firm, moonlights on Queer Eye, and recently brokered his first bedding deal with Target.
Having come up in the industry through a series of audacious bets on himself, Brent has developed a sense of humor and pragmatism around his relationship with creativity and his role as a founder, designer, and collaborator. Hes quick to poke fun at himself, noting that hes working on his control issues. (If I had it my way Id touch every hinge, every doorknob, every finish.) And hes clear that he absorbs as much as he can to consistently shape and influence his creative output: from a personal archive of design magazines to pop culture. (I watch terrible, terrible TV.)
As Brent enters the second decade of Jeremiah Brent Design, he says his relationship with design and creativity has become more rooted in storytelling, informed by the clients he works for and the team he works with.
As time goes on, my work is known for a real kaleidoscope of design styles, Brent says. Everybody is so different, and their stories and their narratives are so different. I really want to be known as somebody who executes your story, not somebody who executes what I do really well. I don’t want to be one thing.
I’m an early riser. I don’t need a ton of sleep. I usually get up around 4 or 4:30 a.m. I have the mornings to myself; my kids are all sleeping. I’ve got three hours of uninterrupted silence with far too much coffee. Music on, candles lit, and I work. A lot of times, I write, which is new.
I didnt start with a degree in design. It really was just one of those things that happened through osmosis. When I started the firm, I wanted it to be me and like five people sitting around the desk, dreaming up the most insane spaces, the most beautiful things.
[Photo: Trevor Tondro]
I’m super visual. My office is like a serial killer. A controlled serial killer.
I’m creatively always hungry. I’m always pulling and looking. I’m particularly inspired right now by the contrast and conflict between design styles and materials. When you bridge what was going on in, like, France in the 1930s with what was happening in the States in the 1980s? I think that conflict, and that contrast is where all the original ideas lie.
Somebody asked me, Do you think taste is genetic? I don’t think taste is a recessive gene. I think it has so much to do with curiosity, audacity, travel, absorbing.
At my core, Im a good storyteller. Thats really where my strength is. I can listen. I can hear the nuances of what people need, and sometimes theyre not even saying it. That was the basis for the firm. I didnt imagine it growing to the scale it has. Even though the company is 50-plus people, we still have that same synergy of five people sitting down at a table. There are so many different ways to make something beautiful. So that’s where I’m at now. It’s defining my lane of creativity and how I participate, how I nurture the creativity of my team.
[Photo: Trevor Tondro]
I always feel the most creative when I’m with the people I’m creating for. The biggest part of it is getting to know the people and understanding where they’re from. What was the first room that ever held you? What was the most important space that you remember? At least this part of the creativity, for me, is earning people’s trust. It’s something that you’re not given. You’ve gotta earn it.
The fantasy part of what I do is where the love story is. So I always kind of call out one of the most important moments of your day. Where does it start? Where is the middle? Where does it end? And that acts as the beginning of the ripple. You build from there. You know, the fantasy, that component of that conversation with a client assures them that you understand what they value. And then I work backwards.
I sketch everything. I have to see the space and how you’re going to move through it first before I dig into the intricacies of breaking everything down. It’s all visual. So I’ll draw everything, build the space out, prioritize. It’s changed over time, and it changes with clients, but you know, it’s always a conversation around what matters most to the client.
I’ve never said no to work, even when I should. This was the first year that I’ve had to be like, Okay, well, we can’t do that yet. Or That’s not gonna work. That feels weird to me. I feel a pivotal shift in my tenacious appetite for growth. The evolution becomes everybody else’s, too. It’s not just mine now. So Im making sure Im executing and illustrating the balance that I want everybody else to have in their life. I joke all the time with everybody I work with. I want you to make a lot of money, and I want you to love what you do.
[Photo: Trevor Tondro]
I just need to move and to travel, sometimes. We live in New York City . . . but then we have this farm in Portugal. I realized this year that I live between two extremes: I need the volume turned all the way up, or I need to go to Portugal, where the volume is completely turned down and nurtures me in a way that I never even thought was possible. In Portugal, Im a nighttime person, and in New York, I’m a morning person. Each gives me different things.
I think trends are great if you’re not beholden to them. It’s a great way to have a conversation. It’s a great way to travel visually and maybe look at something that you would not have normally seen. To use them as a marketing tool is annoying. Just because turquoise is a hot color right now doesn’t mean you need to paint your room turquoise. But let’s examine turquoise. What do we like about it? Where did it start? It’s fun.
Ive had a crash course on how to collaborate because I married another interior designer. Which I do not suggest, because there are a lot of opinions from gay decorators in the house. I think it was an interesting exercise for me, because, especially creatively, if I had my way with our home, it would be dark with one dimly lit room with one bowl on a table. Very wabi-sabi. It’s my husband’s worst nightmare. He would live in, like, you know, a French château. Hes like Marie Antoinette. So, we have found a balance and a joint style that works for the both of us.
I’m not pretending that I’m the most talented person in the room. I may be the most passionate, but definitely not the most talented, and I’ve seen so many different times from collaborations how far you can take a project with other people.
Early in my career, a colleague and I made a shared commitment one summer to eat healthier. Salads. Smoothies. The full routine. Like many well-intentioned plans, our discipline began to fade after a few weeks. Eventually, we introduced what we jokingly called Grease Wednesdays, a weekly cheat day as a reward for all our good behavior.
Every Wednesday, one of us would head out to grab fast food, and wed hide away in a small boardroom to indulge in our shared lack of nutritional discipline. At first, it was just the two of us, chatting with laptops closed and fries on the table.
And then coworkers began peeking into whatever boardroom we were in, curious about the laughter. Eventually, someone asked if they could join. Then another. Within weeks, we had outgrown the small meeting room. Within months, we had moved into the departments largest boardroom to accommodate the growing crowd.
What started as a casual indulgence became a shared ritual. And without intending to, Grease Wednesdays began to change our department culture. We all began to get to know each other as individuals, with pets and families and hobbies. The ritual also smoothed tensions between departments, built friendships between unfamiliar teammates, and helped us realize we hadnt felt all that connected before.
Recent research shows the disconnection I witnessed in my own team is now part of a broader workplace trend. A 2025 survey of U.S. workers found nearly 40% report feeling lonely at work, and employees who lack social connection are significantly more likely to consider leaving their jobs because of it.
When people feel they belong, trust builds, collaboration accelerates, performance rises, loyalty deepens, and well-being improves. When they dont, silos form, trust erodes, and discretionary effort fades. Take these numbers: a recent BetterUp survey found that workplace belonging leads to a 56% increase in job performance, a 50% reduction in turnover risk, and a 75% decrease in employee sick days.
THE PROBLEM WITH OVER-ENGINEERING CONNECTION
Belonging is not accidental; its cultural. And culture is shaped, reinforced, and protected by a leaders vision, values, behavior, and accountability, including what I call positive accountability. But this is where many organizations misstep. When leaders notice disconnection, the instinct is often to formalize solutions with more engagement meetings, structured team building, and mandatory social events.
Yet forced connection and fun rarely produce authentic trust. In fact, over-engineering connection can make people more guarded. For instance, research cited in a study by the University of Sydney found that when team-building activities feel mandatory, they can create resentment and pushback among employees. Belonging grows best in environments that feel natural, voluntary, and human, not observed or measured.
If you want to improve connection and belonging in your workplace while avoiding forced connection, here are some steps you can take.
DESIGN INTENTIONAL SPACES
What made Grease Wednesdays powerful wasnt the food. It was the opportunity that a casual ritual created. We had, quite by accident, built a small, repeatable, low-pressure interaction in which familiarity could grow.Design offers a strong middle ground between compulsory team-building exercises and complete social neglect. The key here is to design small, optional, and repeatable opportunities that humanize the workplace.
For in-person teams, you can host walking one-on-one meetings, Friday coffee drop-ins, no-agenda team lunches, or cross-department donut runs. For remote teams, you could host 15-minute morning online coffee drop-ins or no-agenda team virtual lunches, and share team celebrations of birthdays, anniversaries, and project completions. Keep it light; keep it optional; keep it ritual.
MODEL OPENNESS
Studies in organizational research find that when leaders are open, available, and accessible, employees feel more psychological safety. Psychological safety, coined by organizational psychologist Amy Edmondson, is the shared belief within a team that it is safe to take interpersonal risks, like speaking up with ideas, questions, concerns, or mistakes, without fear of punishment, humiliation, or retribution.To build psychological safety in teams, leaders can model openness. Do that by admitting when you dont know something, sharing a decision youve reversed (and why), and publicly thanking a team member who challenged you.
Another way you can model openness is by offering positive team accountability by sharing the successes they see and are proud of within the team. For example, one leader I work with sends out an email to his team every two or three weeks. The irregularity of timing is actually effective by design, making the email feel more authentic.
REWARD CONNECTION, NOT JUST OUTPUT
Social psychology research shows that reciprocity in the workplace builds trust, cooperation, and positive relationships. The principle of social reciprocity, or when one recognizes and responds to positive actions, contributes to stronger workplace dynamics and mutual respectthe core components of connection and belonging.One way to do this is to shift what gets publicly praised. If the only Slack shout-outs are for revenue, speed, and delivery, people will assume that is all that matters.
Instead, reward connection by recapping projects in team meetings by asking, Who helped make this possible? You can also celebrate the people who mentor, unblock, and build bridges across teams. When helping behavior is acknowledged, rewarded, and career-relevant, connection stops being invisible labor and becomes part of how success is defined.
Full offices dont cure loneliness, but intentional culture does. When leaders design natural rituals, model openness, and reward connection as deliberately as they reward performance, belonging is no longer accidentaland becomes part of how work actually works.
Being a middle manager often feels like living in two worlds at once. On one side, executives cascade big goals and sweeping strategies. On the other, teams look to you for clarity, advocacy, and daily guidance. Youre constantly reconciling top-down demands with bottom-up realities, often with too little time and too few resources to satisfy either side.
The paradox of the role is stark: Middle managers carry enormous responsibility for execution but dont always have the authority to make critical decisions. Youre expected to deliver results on budgets you dont control, within structures you didnt design, and through policies you didnt write. This tension is one of the biggest sources of chronic strain.
One survey found that middle managers reported higher burnout rates (36%) than non-managers, while another showed that 71% are sometimes or always overwhelmed at work. But heres the good news: The middle isnt just where pressure piles up. Its also where strategy becomes reality, where culture is lived (or lost), and where agility gets tested in real time. If you can reframe the squeeze as an opportunity, middle management becomes less a grind and more a proving ground.
Here are four ways to turn the pressure into potential:
BUILD YOUR COALITION
If you think of your team only as your direct reports, youre missing the larger playing field. Work today is inherently cross-functional, which means your effectiveness hinges on your ability to influence sideways and upward, not just to manage downward. Peers hold the resources and expertise you need. Leaders above you control priorities, approvals, and air cover. Without credibility in those directions, even flawless execution within your own group can collapse at the edges.
Research shows that misalignment between teams is one of the biggest drivers of wasted work. When priorities or interpretations differ, teams can spend weeks pulling in opposite directions. Middle managers who proactively build peer alignment surface these gaps early and save everyone time and frustration.
The fix isnt complicated, but it is intentional: cultivate your network. A short, well-timed conversation with a peer or senior leader can prevent the kind of breakdowns that leave your team spinning. Think of it less as networking and more as preemptive damage control. The middle managers who thrive are the ones who invest in relationships that make the work move.
MASTER THE PRACTICE OF LEADERSHIP
Leadership is often packaged as a set of sweeping competencies or treated like a fixed trait you either have or dont. In reality, leadership is shaped over time, forged through daily choices, interactions, and repeated practice.
While traditional leadership development focuses on broad skills taught in workshops or courseswhat we call horizontal development at Sounding Boardmany real-world challenges require something deeper. Vertical development helps managers think more complexly, adapt to evolving contexts, and lead with lasting impact, not just quick fixes.
This kind of development happens through practice, not theory. Neuroscience supports it: Consistent, real-world repetition strengthens the neural pathways that anchor adaptability and retention. At BTS, weve seen that transformational leadership often hinges on unlocking specific mindset shifts, patterns where leaders typically get stuck and need to evolve to grow.
So, how do you start? Find smaller moments to experiment. Instead of waiting for a performance review, try a quick debrief after a call with a direct report. Test a new communication approach in a team meeting before the next town hall. You can even name your intention to those around you. Letting others know youre trying something new sets expectations and invites helpful feedback.
LEVERAGE AI FOR ON-DEMAND SUPPORT
Your toughest challenges dont show up as theory; they show up in the form of messy, human situations: a disengaged direct report, a senior leader who keeps moving the goalposts, a peer who wont align. These problems dont have one-size-fits-all solutions, which is why coaching is so powerful.
For decades, personalized coaching was a privilege reserved for executives. But with AI practice bots paired with guidance from real coaches, middle managers can get development thats personalized and scalable when they need it.
These tools let you rehearse tough conversations, like giving feedback or delegating more effectively, in a low-stakes environment. Coaches help you translate insights into actions and longer-term mindset shifts. The result is leadership growth thats less abstract and more actionable.
The smartest move? Start small. Pick one conversation youve been avoiding and rehearse it with an AI conversation bot. Youll uncover blind spots, test new approaches, and walk into the real thing with more confidence and control.
MAKE UNCERTAINTY YOUR PLAYGROUND
The defining condition of modern work is uncertainty. Markets swing, technologies disrupt, priorities pivot. If you wait for clarity, youll always be behind. The managers who thrive arent the ones who resist ambiguity, but those who use it as a catalyst to experiment and learn.
One biopharmaceutical company I worked with recognized this when it expanded leadership development beyond senior executives to include middle managers. After providing leadership training focused on managing ambiguity and integrating AI into workflows, the company paired each manager with a coach to help translate learning into action. The result was faster decision-making and stronger cross-functional collaboration during a major pivot.
When you stop treating uncertainty as a threat and start treating it as a laboratory, you shift from surviving change to shaping it.
With these practices, middle management isnt a burden, but a launchpad for growth.
We’ve been sold a lie. Somewhere between go to school and get a job, work became the central node of our livesthe very thing that defines us. We measure our worth by our output, our identity by our title, and our health by how much we can endure. The hours. The travel. The back-to-back meetings. The busyness.
That’s not the picture we painted for ourselves when we chose our major in college and envisioned what we thought would be a fulfilling career; that’s conditioning. The result of which has shaped our meaning of work and how we see ourselves in it. But meaning isnt found in the busyness of the grindrather, it’s found in alignment. And when our work has greater meaning, we change our relationship with it and, more importantly, with ourselves.
On our latest episode of the From the Culture podcast, we spoke with Lenore Skenazy, cofounder and president of the nonprofit Let Grow, about finding meaning at work. And she offered a unique framing for how to rethink work and find alignment. In response to the public backlash she received after penning a 2008 column in the New York Daily News about letting her 9-year-old son ride the New York City subway alone, Skenazy founded Let Grow with NYU business school professor Jonathan Haidt to help parents rethink the job of parenting.
In our venture to become parents, we didnt imagine our job would be that of a supervisor or a concierge to our children. Instead, we imagined ourselves as guardians who would help our children grow. For Skenazy, the meaning of parenting is to prepare our children for adulthood, not to protect them from it.
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A deep rethink
Although this may seem like a simple repositioning, its actually a profound recontextualization. When we think about parenting as a job of preparation as opposed to protection, it gives our work new meaning and, as a result, we engage in it differently. As Skenazy argues, when the work of parenting is about preparation, we grant our children freedom and independence to navigate the world on their own. Not in a way that endangers them but, rather, challenges them. When this happens, not only do they grow into more resilient humans who will likely be better prepared for the world, but weas parentsget more fulfillment from our work.
The benefit of this recontextualization also applies to our professional work. When we reframe the meaning of work, we change our alignment with it. The result of this framing not only improves our well-being but also improves the work. The behavioral science is unambiguous to this fact. When work is more meaningful, were more engaged, more committed, and more satisfied. Moreover, these effects produce greater productivity and higher effort because were more willing to go the extra mile when we feel more fulfilled.
A win-win
This phenomenon happens on the individual level but scales when we consider the greater work of the organization. When workers collaborate in shared meanings, their collective outputs are optimized, and the organization is more likely to flourish because of it. This isnt about touchy-feely, woo-woo vibes to make people feel good. This is a renegotiation of work that empirically changes how we work, the impact of our work on the organization, and its impact on us. Its a win-win across the board.
But thats not the world of work we occupy. Instead, our current framing of work is one that valorizes grind and prioritizes compensationwhich is transactional at best, but in most cases adversarial. Thats not to say that labor should not be sufficiently compensated, but that the exchange between wages and work should be more than just monetary. They should be meaningful as well.
Suffice it to say that work is in desperate need of work. Not more grind, more hours, or more late nights, but more meaning. The best part about it is that meaning is socially negotiated and, therefore, we can change it ourselves. It doesnt require permission or approvaljust rethinking. We explore this in greater depth with Skenazy on our latest episode of From the Culture, available here or wherever you get your podcasts.
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