Extractive summaries and key takeaways from the articles carefully curated from TOP TEN BUSINESS MAGAZINES to promote informed business decision-making | Since 2017 | Week 431, covering December 12-18, 2025 | Archive
Shaping Section
Look around: Bubbles are everywhere.
By Brad Stone | Bloomberg Businessweek | December 17, 2025
2 key takeaways from the article
- Today there are many warning about AI bubble. But if you define a speculative bubble as any phenomenon where the worth of a certain asset rises unsustainably beyond a definable fundamental value, then bubbles are pretty much everywhere you look. There may be a bubble in gold, whose price has soared almost 64% in the year to Dec. 12, and one in government debt as nations collectively haven’t operated this deeply in the red since World War II. Many financiers believe there’s a bubble in private credit, the $3 trillion market in loans by large investment houses. The most obvious absurdities have materialized, where there’s no easy way to judge an asset’s intrinsic worth. The total market value of Bitcoin, for example, rose $636 billion from the start of the year through Oct. 6—before losing all of that and more, as of Dec. 12. In food there’s most certainly a protein bubble, with everyone from the makers of popcorn to breakfast cereal marketing their protein content to appeal to health-conscious consumers and GLP-1 users.
- The stakes are higher for AI than they are for any other, of course. The cumulative impact of an AI bubble begins to look more severe. “When we have entities building tens of billions worth of data centers based on borrowed money without real customers, that is when I start worrying,” says Gil Luria, managing director at investment firm D.A. Davidson & Co., evoking Roger Babson from a century ago. “Lending money to a speculative investment is never a good idea.”
(Copyright lies with the publisher)
Topics: Global Economy, Bubbles in Global Economy
Extractive Summary of the Article | Read | Listen
Two months before Black Monday, the market crash that led to the Great Depression, a Massachusetts economist named Roger Babson, fretting over a wave of mom-and-pop investors borrowing money to buy stocks, declared in a speech that “sooner or later a crash is coming and it may be terrific.” Afterward, the market sank 3%, a dip known at the time as the “Babson Break.” But in the weeks that followed, writes Andrew Ross Sorkin in his engrossing new history, 1929: Inside the Greatest Crash in Wall Street History—and How It Shattered a Nation, “the market shook off Babson’s foreboding,” in part because of optimism over new mass-market products like the radio and the automobile. “Investors with an ‘imagination’ were winning again.”
Today there are many Cassandras like Babson warning about AI, in particular the valuation of public and private technology companies and their headlong pursuit of the elusive goal of artificial general intelligence (systems that can do just about anything a human can do and more). Tech companies are on pace to spend just shy of $1.6 trillion annually on data centers by 2030, according to data analyst Omdia. The magnitude of the hype around AI, whose prospects as a profit maker remain entirely hypothetical, has confounded many sober-minded investors. Yet now, as a century ago, the idea of missing out on the next big thing has persuaded many companies to ignore such prophecies of doom. They “are all kind of playing a game of Mad Libs where they think these moonshot technologies will solve any existing problem,” says Advait Arun, a climate finance and energy infrastructure analyst at the Center for Public Enterprise, whose recent Babsonesque report, Bubble or Nothing, questioned the financing schemes behind data center projects. “We are definitely still in the irrational exuberance stage.”
Journalists usually would be wise to abstain from debating whether a resource or technology is overvalued. According to the author he has no strong opinion on whether we’re in an AI bubble, but he wonders if the question may be too narrow. If you define a speculative bubble as any phenomenon where the worth of a certain asset rises unsustainably beyond a definable fundamental value, then bubbles are pretty much everywhere you look. And they seem to be inflating and deflating in lockstep.
There may be a bubble in gold, whose price has soared almost 64% in the year to Dec. 12, and one in government debt, according to Børge Brende, chief executive officer of the World Economic Forum, who recently observed that nations collectively haven’t operated this deeply in the red since World War II. Many financiers believe there’s a bubble in private credit, the $3 trillion market in loans by large investment houses (many for the purpose of building AI data centers) that’s outside the heavily regulated commercial banking system. Jeffrey Gundlach, founder and CEO of money-management firm DoubleLine Capital, recently called this opaque, unregulated free-for-all “garbage lending” on the Bloomberg podcast Odd Lots. Jamie Dimon, JPMorgan Chase & Co.’s CEO, dubbed it “a recipe for a financial crisis.”
The most obvious absurdities have materialized, where there’s no easy way to judge an asset’s intrinsic worth. The total market value of Bitcoin, for example, rose $636 billion from the start of the year through Oct. 6—before losing all of that and more, as of Dec. 12. The trading volume of memecoins, those virtual contrivances that commemorate online trends, peaked at $170 billion in January, according to crypto media firm Blockworks, but by September had collapsed to $19 billion. Leading the decline were the $TRUMP and $MELANIA coins, launched by the first family two days before Inauguration Day, which have lost 88% and 99% of their worth, respectively, since Jan. 19.
In food there’s most certainly a protein bubble, with everyone from the makers of popcorn to breakfast cereal marketing their protein content to appeal to health-conscious consumers and GLP-1 users. In media there just might be a bubble in Substack newsletters, celebrity-hosted podcasts (Amy Poehler’s Good Hang and Meghan Markle’s Confessions of a Female Founder) and celebrity-focused documentary biopics authorized by their subjects and available to stream nearly every week (the latest on Netflix: Being Eddie on Eddie Murphy, and Victoria Beckham). “Everyone’s reference group is global and goes far beyond what they can see around them and beyond what their actual class or position is,” says W. David Marx, the author of Blank Space: A Cultural History of the Twenty-First Century. “You can have globally aligned movements within these markets that in the past would have been impossible.”
The stakes are higher for AI than they are for Labubus, of course. No company wants to be left behind, and so every major player is plowing forward, building computing infrastructure using complex financing arrangements. In some cases this involves a special purpose vehicle (remember those from the 2008 financial crash?) loaded with debt to buy Nvidia Corp. graphics processors, the AI chips that some observers think may depreciate more quickly than expected.
The tech giants can weather any fallout from this FOMO-induced stampede. They’re paying for their data centers largely from their robust balance sheets and can navigate the consequences if white-collar workers all decide that, say, the current version of ChatGPT is plenty good enough to craft their annual self-evaluation. But other companies are engaging in riskier behavior. Oracle Corp., a stodgy database provider and an unlikely challenger in the AI rush, is raising $38 billion in debt to build data centers in Texas and Wisconsin.
Other so-called neoclouds, relatively young companies such as CoreWeave Inc. and Fluidstack Ltd. building specialized data centers for AI, Bitcoin mining and other purposes, are also borrowing heavily. Suddenly the cumulative impact of an AI bubble begins to look more severe. “When we have entities building tens of billions worth of data centers based on borrowed money without real customers, that is when I start worrying,” says Gil Luria, managing director at investment firm D.A. Davidson & Co., evoking Roger Babson from a century ago. “Lending money to a speculative investment is never a good idea.”
Carlota Perez, a British-Venezuelan researcher who’s been writing about economic boom and bust cycles for decades, is concerned as well. She says innovation in tech is being turned into high-stakes speculation in a casino economy that’s overleveraged, fragile and prone to bubbles ready to pop as soon as active doubt begins to spread. “If AI and crypto were to crash, they are likely to trigger a global collapse of unimaginable proportions,” she wrote in an email. “Historically, it is only when finance suffers the consequences of its own behavior, instead of being perpetually bailed out, and when society reins it in with proper regulation, that truly productive golden ages ensue.” Until then, hold your Labubus tightly.
The great AI hype correction of 2025
By Will Douglas Heaven | MIT Technology Review | December 15, 2025
2 key takeaways from the article
- Some disillusionment was inevitable. When OpenAI released a free web app called ChatGPT in late 2022, it changed the course of an entire industry—and several world economies. Millions of people started talking to their computers, and their computers started talking back. We were enchanted, and we expected more. Look how the line goes up! Generative AI could do anything, it seemed. Well, 2025 has been a year of reckoning.
- AI is really good! And yet you can’t help but wonder: When the wow factor is gone, what’s left? How will we view this technology a year or five from now? Will we think it was worth the colossal costs, both financial and environmental? With that in mind, here are four ways to think about the state of AI at the end of 2025: The start of a much-needed hype correction. LLMs are not everything. AI is not a quick fix to all your problems. Are we in a bubble? (If so, what kind of bubble?) And ChatGPT was not the beginning, and it won’t be the end.
(Copyright lies with the publisher)
Topics: AI, Human & Technology
Extractive Summary of the Article | Read | Listen
Some disillusionment was inevitable. When OpenAI released a free web app called ChatGPT in late 2022, it changed the course of an entire industry—and several world economies. Millions of people started talking to their computers, and their computers started talking back. We were enchanted, and we expected more.
We got it. Technology companies scrambled to stay ahead, putting out rival products that outdid one another with each new release: voice, images, video. With nonstop one-upmanship, AI companies have presented each new product drop as a major breakthrough, reinforcing a widespread faith that this technology would just keep getting better. Boosters told us that progress was exponential. They posted charts plotting how far we’d come since last year’s models: Look how the line goes up! Generative AI could do anything, it seemed. Well, 2025 has been a year of reckoning.
For a start, the heads of the top AI companies made promises they couldn’t keep. They told us that generative AI would replace the white-collar workforce, bring about an age of abundance, make scientific discoveries, and help find new cures for disease. FOMO across the world’s economies, at least in the Global North, made CEOs tear up their playbooks and try to get in on the action.
That’s when the shine started to come off. Though the technology may have been billed as a universal multitool that could revamp outdated business processes and cut costs, a number of studies published this year suggest that firms are failing to make the AI pixie dust work its magic. Surveys and trackers from a range of sources, including the US Census Bureau and Stanford University, have found that business uptake of AI tools is stalling. And when the tools do get tried out, many projects stay stuck in the pilot stage. Without broad buy-in across the economy it is not clear how the big AI companies will ever recoup the incredible amounts they’ve already spent in this race.
At the same time, updates to the core technology are no longer the step changes they once were.
The highest-profile example of this was the botched launch of GPT-5 in August. Here was OpenAI, the firm that had ignited (and to a large extent sustained) the current boom, set to release a brand-new generation of its technology. OpenAI had been hyping GPT-5 for months: “PhD-level expert in anything,” CEO Sam Altman crowed. On another occasion Altman posted, without comment, an image of the Death Star from Star Wars, which OpenAI stans took to be a symbol of ultimate power: Coming soon! Expectations were huge.
And yet, when it landed, GPT-5 seemed to be—more of the same? What followed was the biggest vibe shift since ChatGPT first appeared three years ago. “The era of boundary-breaking advancements is over,” Yannic Kilcher, an AI researcher and popular YouTuber, announced in a video posted two days after GPT-5 came out: “AGI is not coming. It seems very much that we’re in the Samsung Galaxy era of LLMs.”
A lot of people (me included) have made the analogy with phones. For a decade or so, smartphones were the most exciting consumer tech in the world. Today, new products drop from Apple or Samsung with little fanfare. While superfans pore over small upgrades, to most people this year’s iPhone now looks and feels a lot like last year’s iPhone. Is that where we are with generative AI? And is it a problem? Sure, smartphones have become the new normal. But they changed the way the world works, too.
To be clear, the last few years have been filled with genuine “Wow” moments, from the stunning leaps in the quality of video generation models to the problem-solving chops of so-called reasoning models to the world-class competition wins of the latest coding and math models. But this remarkable technology is only a few years old, and in many ways it is still experimental. Its successes come with big caveats.
Perhaps we need to readjust our expectations.
Let’s be careful here: The pendulum from hype to anti-hype can swing too far. It would be rash to dismiss this technology just because it has been oversold. The knee-jerk response when AI fails to live up to its hype is to say that progress has hit a wall. But that misunderstands how research and innovation in tech work. Progress has always moved in fits and starts. There are ways over, around, and under walls.
Take a step back from the GPT-5 launch. It came hot on the heels of a series of remarkable models that OpenAI had shipped in the previous months, including o1 and o3 (first-of-their-kind reasoning models that introduced the industry to a whole new paradigm) and Sora 2, which raised the bar for video generation once again. That doesn’t sound like hitting a wall to me.
AI is really good! Look at Nano Banana Pro, the new image generation model from Google DeepMind that can turn a book chapter into an infographic, and much more. It’s just there—for free—on your phone
And yet you can’t help but wonder: When the wow factor is gone, what’s left? How will we view this technology a year or five from now? Will we think it was worth the colossal costs, both financial and environmental?
With that in mind, here are four ways to think about the state of AI at the end of 2025: The start of a much-needed hype correction. LLMs are not everything. AI is not a quick fix to all your problems. Are we in a bubble? (If so, what kind of bubble?) And ChatGPT was not the beginning, and it won’t be the end.
Strategy & Business Model Section
Inside OpenAI’s fragile lead in the AI race, and the 8-week ‘code red’ to fend off a resurgent Google
By Jeremy Kahn | Fortune Magazine | December 17, 2025
3 key takeaways from the article
- At this time last December, OpenAI was dazzling the world with its “12 days of Shipmas,” a cleverly marketed daily drumbeat of new product releases that included its paradigm-setting 01 reasoning model. OpenAI had just raised $6.6 billion in fresh funding, and its ChatGPT user base was growing so fast that the company complained it was struggling to find enough computing power to keep up with demand.
- Fast-forward just one year to this December when employees got a decidedly different holiday present: a five-alarm “code red” laid out in a memo from CEO Sam Altman, bracing the team for “rough vibes” and economic headwinds in the wake of increased competition, and trying to light a fire under them to refocus over the coming weeks.
- The company is not in a life-threatening crisis. But the code red alert reveals a real concern within OpenAI that the $500 billion company could lose its position as the standard-bearer and pacesetter for generative AI technology. The data backs that up. While ChatGPT still holds a big lead with more than 800 million weekly users, Google’s Gemini is gaining fast.
(Copyright lies with the publisher)
Topics: Competion in AI, Google vs Open AI
Extractive Summary of the Article | Read | Listen
At this time last December, OpenAI was dazzling the world with its “12 days of Shipmas,” a cleverly marketed daily drumbeat of new product releases that included its paradigm-setting 01 reasoning model. OpenAI had just raised $6.6 billion in fresh funding, and its ChatGPT user base was growing so fast that the company complained it was struggling to find enough computing power to keep up with demand.
Fast-forward just one year to this December when employees got a decidedly different holiday present: a five-alarm “code red” laid out in a memo from CEO Sam Altman, bracing the team for “rough vibes” and economic headwinds in the wake of increased competition, and trying to light a fire under them to refocus over the coming weeks.
As part of the missive, Altman announced a temporary postponement of many of the initiatives that had promised to get OpenAI closer to its stated financial goal of breaking even by 2030. That includes delaying several major revenue generators that the company was counting on: advertising, an expanded e-commerce offering, and (to a lesser extent) agentic systems.
The company is not in a life-threatening crisis. ChatGPT topped Apple’s App Store charts as the most downloaded free app in the U.S. in 2025, and just last week, OpenAI announced a groundbreaking partnership with Walt Disney Co. that will bring the media company’s characters to OpenAI products (along with a $1 billion investment). But the code red alert reveals a real concern within OpenAI that the $500 billion company could lose its position as the standard-bearer and pacesetter for generative AI technology.
The data backs that up. While ChatGPT still holds a big lead with more than 800 million weekly users, Google’s Gemini is gaining fast: In its Q3 earnings, Google’s parent company Alphabet announced that its Gemini app now has 650 million monthly active users, up from 450 million in July. Preliminary November data from SimilarWeb reveals Google Gemini generated 1.351 billion website visits—a 14.3% increase from October. Meanwhile, ChatGPT fell below the 6 billion benchmark it had touched in October, recording 5.844 billion visits and marking its second month-over-month decline in 2025.
In the lucrative market for enterprise customers, OpenAI appears to have lost significant market share, falling to 27% according to one recent report by Menlo Ventures, while Gemini has risen to 21% and rival startup Anthropic leads at 40%. (OpenAI disputes the figures from Menlo, noting that the venture capital firm is an investor in Anthropic; it notes that more than 1 million business customers now use OpenAI tools, with sharp upticks in usage metrics over the past year. The company also just released a survey of its enterprise users that showed enterprise usage of OpenAI’s models more than doubling over the past year in many countries outside the U.S.)
The internal call to arms lays bare the very precarious position this market leader is now in, particularly as it confronts industry titans like Google (as well as Microsoft and Meta), with tens of billions of dollars in cash on their balance sheets and massive ecosystems of products to boost their distribution.
For Altman, a longtime tech entrepreneur, the historic matchups of Silicon Valley’s past, pitting innovators and incumbents in winner-takes-all battles, are surely contributing to the sense of urgency: The annihilation of browser pioneer Netscape by Microsoft or the eclipse of BlackBerry’s handheld communications gadgets by Apple’s iPhone comes to mind. But there’s also the example set by Facebook founder Mark Zuckerberg, whose famous “lockdowns” over a decade ago helped repel the threat of Google’s nascent—and ultimately doomed—social networking product.
The decisions made by OpenAI and its competitors at this critical juncture in a fast-moving market will decide which company cements its hold on what some have called the most transformative technology since electricity, and which will end up as odd footnotes in the final writing of the history of AI.
How Procter & Gamble Uses AI to Unlock New Insights From Data
By Thomas H. Davenport and Randy Bean | MIT Sloan Management Review | December 17, 2025
3 key takeaways from the article
- Few organizations can legitimately claim to have been doing analytical research for over a century. P&G was also a pioneer in the creation of common data across a global organization, establishing a large group of analytical professionals and then including them in business units. It has maintained a tradition of using customer and market insights into the present, and it’s now using analytical, generative, and agentic AI to address important business issues.
- To speed up AI model creation, P&G developed a capability that it calls an “AI factory.” It provides a vehicle — including data sources, software tools, methods, and defined security protocols — to rapidly develop, test, deploy, and monitor algorithms in production. While P&G’s generative AI products are focused on personal productivity, the company is equally interested in use cases that can directly scale into business processes.
- P&G has long had a focus on building human capabilities in data, analytics, and AI. The company has partnered with Harvard Business School and Boston Consulting Group for AI upskilling across the P&G workforce.
(Copyright lies with the publisher)
Topics: AI Strategy, Business Operations, Technology Adoption
Extractive Summary of the Article | Read | Listen
Few organizations can legitimately claim to have been doing analytical research for over a century. P&G was also a pioneer in the creation of common data across a global organization, establishing a large group of analytical professionals and then including them in business units. It has maintained a tradition of using customer and market insights into the present, and it’s now using analytical, generative, and agentic AI to address important business issues.
To find out what P&G is up to with artificial intelligence, the authors spoke with Jeff Goldman, vice president of enterprise data science and leader of business AI initiatives at the company who has assembled a group of several hundred data scientists and AI engineers who build and deploy AI algorithms at scale across the company’s marketing, digital commerce, supply chain, and sales organizations. Most of the data scientists are directly embedded in business units or AI product teams.
Around 2021, Goldman and his colleagues observed that AI was playing an increasingly strategic role in P&G’s business. The complexity of algorithms in production was growing, and the delays involved in progressing from prototype to scale with AI algorithms were having a material financial impact. Algorithms were being developed in a bespoke manner, and each one required custom deployment by the company’s AI engineers.
To speed up AI model creation, P&G developed a capability that it calls an “AI factory.” It provides a vehicle — including data sources, software tools, methods, and defined security protocols — to rapidly develop, test, deploy, and monitor algorithms in production. The platform to allow people instant access to the data within the data repository, but then also instant access to the AI algorithms and generative models. Therefore, a developer spends a lot less time worrying about ‘How do I scale it?’ — because that comes out of the box.
Goldman said that the AI factory reduces the time to model deployment by roughly six months. He also pointed out that the factory evolves as technology does. For example, it now incorporates agentic AI capabilities, including monitoring agentic systems at scale, registering agents, and applying the requisite Agent2Agent Protocol and the Model Context Protocol to connect multiple agents and tools. Goldman said there are basically two sides to the company’s AI Engineering organization: One is responsible for building and continuously updating the factory, while the other focuses on scaling and operating the algorithms developed within it.
The AI factory capabilities facilitate testing different versions of models for specific business requirements.
Goldman noted that a huge percentage of value has and will continue to come from analytical AI, citing use cases involving supply chain management and media decisioning. But P&G was also early to embrace generative AI, and it has a variety of internal product offerings in place that employ that technology. After providing employees with secure access to a variety of underlying large language models that can be selected based on the business issue being addressed (through a product it has dubbed chatPG), the company has developed imagePG and askPG. ImagePG supports the generation and analysis of images and videos, including for the company’s advertising, while askPG incorporates curated internal unstructured data for use by employees.
While P&G’s generative AI products are focused on personal productivity, the company is equally interested in use cases that can directly scale into business processes. For example, the company’s Great Idea Generator tool creates product and advertising concepts based on consumer trends and previous testing results. It can greatly accelerate the progress of a new idea, from concept to advertisement to shelf. Another AI tool is Project Genie, which synthesizes information from articles and help documents to provide information to over 800 customer service reps, greatly reducing question processing time.
P&G has long been focused on providing easy access to data on how different aspects of the business are performing. Business Sphere (the analytics visualization environment) and a desktop equivalent called Decision Cockpits were part of an earlier approach to that issue, but now the company offers a generative front end to business data called insightsPG.
P&G’s experiments with agentic AI have focused on a series of pilots to determine where the technology can be employed most effectively. Goldman commented that there have already been early successes with agents across the advertising, supply chain, and consumer relations areas. Maintaining a human in the loop for these and other processes remains critical, he said.
Recently, R&D teams have complemented the work of lab technicians with AI algorithms to enable greater speed in molecular discovery. One example is P&G’s Perfume Development Digital Suite, an ecosystem of digital tools powered by AI and advanced data processing to create new fragrances five times faster than was previously possible. AI algorithms analyze millions of data points and help create perfume character models based on consumer insights into what makes a fragrance smell good. The model integrates perfume character models to identify likely winning formulations that are then tested through fast prototyping and experimentation.
Together with IT, the R&D organization is testing new ways of working using generative AI.
The use of AI also helped break down functional silos between R&D and commercial professionals: Professionals from either background using AI produced more balanced solutions.
P&G has long had a focus on building human capabilities in data, analytics, and AI. The company has partnered with Harvard Business School and Boston Consulting Group for AI upskilling across the P&G workforce.
Personal Developmnet, Leading & Managing Section
How chief risk officers can build the next generation of leaders
By By Farah Dilber et al., | McKinsey & Company | December 10, 2025
2 key takeaways from the article
- The risk function is evolving faster than ever—and so too must the profile of its leaders. Historically, great risk leaders were characterized by deep technical fluency, business acumen, and a strong focus on compliance. While these traits remain essential, today’s disruptive environment—with the acceleration in AI, shifting regulatory priorities, rising geopolitical volatility, and growing complexity and interdependence in the risk landscape—demands even more from risk professionals and creates an opportunity to shape the risk function of the future. It is more important than ever that risk leaders act decisively amid uncertainty, maintain a strong external orientation, and harness technology and analytics to accelerate impact.
- In many ways, the risk function is well positioned to cultivate these traits in individuals. Four actions Chief risk officers (CROs) and their teams are taking to position the risk function as a leadership factory: Develop a clear, forward-looking definition of what makes a great leader in today’s risk environment. Cast a wide net for talent by proactively identifying high-potential leaders within and beyond the risk function. Stretch, upskill, and elevate risk leaders by providing resilience-building experiences through rotations, high-ownership projects, and targeted development. And build sustained capability by hardwiring leadership development into systems, processes, and culture.
(Copyright lies with the publisher)
Topics: Chief Risk Officer, Future Leadership
Extractive Summary of the Article | Read | Listen
The risk function is evolving faster than ever—and so too must the profile of its leaders. Historically, great risk leaders were characterized by deep technical fluency, business acumen, and a strong focus on compliance. While these traits remain essential, today’s disruptive environment—with the acceleration in AI, shifting regulatory priorities, rising geopolitical volatility, and growing complexity and interdependence in the risk landscape—demands even more from risk professionals and creates an opportunity to shape the risk function of the future. It is more important than ever that risk leaders act decisively amid uncertainty, maintain a strong external orientation, and harness technology and analytics to accelerate impact.
In many ways, the risk function is well positioned to cultivate these traits in individuals. Chief risk officers (CROs) and their teams operate across businesses and functions, have a say in most of an organization’s critical decisions while balancing growth and risk considerations, and play a key role in high-priority initiatives, such as regulatory responses, crisis management, and strategic transformations.
As the function evolves to navigate the raft of complexities confronting an organization, its leaders have an opportunity to evolve the risk function as a leadership incubator. Indeed, it can become a place that produces high-performing talent—subject matter experts as well as versatile generalists—equipped to step confidently into enterprise-wide leadership roles.
Drawing on this research as well as McKinsey’s work on developing 21st-century leaders, we have identified four actions CROs and their teams are taking to position the risk function as a leadership factory:
Develop a clear, forward-looking definition of what makes a great leader in today’s risk environment. Based on our research and experience, we believe the 21st-century risk leader embodies five critical traits: Business fluency as a core skill; Emerging technical and domain mastery; distill complex, technical, and ambiguous issues into clear, actionable recommendations so that they can credibly influence the business agenda; develop Orthogonal thinking; and should have Adaptability and resilience.
Cast a wide net for talent by proactively identifying high-potential leaders within and beyond the risk function. Spotting talent requires rigor and constant engagement. Traditional approaches such as structured reviews and HR processes can be systematically leveraged to identify and evaluate talent that meets the vision of a 21st-century risk leader. CROs can also create dedicated forums and programs that surface emerging leaders. These might include talent accelerators for early-career professionals or structured opportunities for rising leaders to present directly to the CRO at least once a year. Some CROs also use informal touchpoints to spot promising talent.
Stretch, upskill, and elevate risk leaders by providing resilience-building experiences through rotations, high-ownership projects, and targeted development. Three ways CROs can do this: cross-pollinate through structured rotations; champion moonshot projects; and create “side of desk” leadership roles.
Build sustained capability by hardwiring leadership development into systems, processes, and culture. This can require a three-pronged approach: Reinforce the risk function’s career value proposition; Be deliberate about mentorship and apprenticeship; and build a culture of learning from both inside and out of the risk function.
Defining a modern leadership profile, broadening the talent aperture, and creating stretch opportunities are critical steps to building the next generation of risk leaders—but they must also be sustained by systems and culture.
A Better Way to Manage Internal Talent Markets
Harvard Business Review Magazine | January–February 2026
3 key takeaways from the article
- Letting your employees decide which tasks they’ll take on seems like an obvious way to boost engagement and retention. But does giving workers more choice improve performance? Or do firms sacrifice productivity when they cede control?
- A recent study compared two approaches within a large organization: firm-dictated assignments and a market-based system, in which employees and managers submitted ranked preferences and an algorithm matched workers to positions. According to the findings manager-led assignments would maximize immediate productivity, while preference-based assignments would maximize employee happiness.
- How can companies increase employee satisfaction by giving workers a say in assignments—without accepting a big hit to productivity? The researchers suggest using a hybrid model, which allows workers to express preferences while giving firms the power to influence or even override employee-task matches. They recommend the following best practices for building an internal talent market that balances worker choice and company needs: provide applicants with better information, offer incentives, and coordinate match decisions.
(Copyright lies with the publisher)
Topics: Internal Talent Markets, Human Resource Management
Extractive Summary of the Article | Read | Listen
Letting your employees decide which tasks they’ll take on seems like an obvious way to boost engagement and retention. That’s one reason why Walmart, the U.S. Army, and other organizations use internal talent markets, which allow employees to explore and apply for various roles, projects, and assignments within their organization. But does giving workers more choice improve performance? Or do firms sacrifice productivity when they cede control?
A recent study by Bo Cowgill of Columbia Business School, Jonathan Davis of the University of Oregon, Pablo Montagnes of Emory University, and Patryk Perkowski of Yeshiva University compared two approaches within a large organization: firm-dictated assignments, in which leaders placed people in roles according to company priorities, and a market-based system, in which employees and managers submitted ranked preferences and an algorithm matched workers to positions. The researchers examined who ended up in each role under the two systems; they also matched employees to jobs randomly as a control. Then they used a statistically validated model the company had created to predict each employee’s productivity based on how closely that person’s skills corresponded to the ones required by the job. They measured how happy the match made the workers (according to the workers’ original preferences). Using this data, they evaluated the trade-off between predicted productivity and job satisfaction.
The results revealed a stark divide: Matches based on company priorities were projected to be 33% more productive than random assignments. Matches that factored in employee preferences were expected to be only 5% more productive than random ones, but employees ranked them 38% more valuable personally. In short: Manager-led assignments would maximize immediate productivity, while preference-based assignments would maximize employee happiness.
This creates an interesting paradox: If happier workers are supposedly more productive, why wouldn’t satisfaction translate into more value for the organization? Researchers found that the shortfall is caused by less-than-ideal matches between the job demands and employees’ skills. This mismatch can result for a variety of reasons. In many cases, the researchers say, descriptions of projects or assignments don’t give enough detail to allow employees to fully understand how well their skills match up. In other cases, employees may choose stretch roles that teach them new skills they can use in future roles, even if those assignments don’t immediately translate into corporate productivity. For workers, this strategy can make career sense. Roughly 90% of the skills workers sought to develop in the internal market were not firm-specific, and getting that type of experience would probably increase employees’ ability to jump to another company. For managers, allowing workers to take those stretch roles helped fill empty slots needed to complete projects but was potentially risky because it made their employees more attractive to other companies.
In many cases the employees didn’t realize they were choosing a task that wasn’t a great fit with their skills. “Projected productivity declined with worker-assigned matches because employees often lacked clear information about their strengths and the company’s priorities,” says Cowgill.
How can companies increase employee satisfaction by giving workers a say in assignments—without accepting a big hit to productivity? The researchers suggest using a hybrid model, which allows workers to express preferences while giving firms the power to influence or even override employee-task matches. They recommend the following best practices for building an internal talent market that balances worker choice and company needs: provide applicants with better information, offer incentives, and coordinate match decisions.
Five Tips To Be A Better Leader In 2026
By Ellen Whitlock Baker | Forbes | December 17, 2025
2 key takeaways from the article
- Living in a time of constant change can feel destabilizing as a leader, especially when you’re constantly confronted with situations where you don’t know the answer, or the answer you would’ve had three years ago won’t help now.
- To be an excellent leader in 2026, adopt a framework of flexibility and nimbleness like never before. Here are five tips that will help you be a better leader in 2026. Question what you know. Embrace collaboration. Create a workplace built on trust. Embrace the pause. And broaden who you’re learning from.
(Copyright lies with the publisher)
Topics: Leadership in 2026, Trust
Extractive Summary of the Article | Read | Listen
Living in a time of constant change can feel destabilizing as a leader, especially when you’re constantly confronted with situations where you don’t know the answer, or the answer you would’ve had three years ago won’t help now. To be an excellent leader in 2026, adopt a framework of flexibility and nimbleness like never before. Throw away your textbooks; we’re writing a whole new workplace.
Here are five tips that will help you be a better leader in 2026.
- Question what you know. Coach and consultant Aiko Bethea’s “anchored, aligned and accountable” framework, as explored in her forthcoming book of the same title, explains that transformative spaces where “we are anchored in our values, our impact is aligned with our values and actions, and we hold ourselves accountable for those impacts” are needed for future work to thrive.
- Embrace collaboration. Many of us, especially women for whom there have historically been fewer spots at the leadership table, grew up thinking that we had to compete with each other to “win” leadership roles, contracts or job opportunities. But when we decide instead to collaborate, we all rise. Ruchika T. Malhotra writes in Uncompete: Rejecting Competition to Unlock Success, “one of the main goals of uncompete is to collectively pool our resources and work together to improve, grow and lead the way for others so that they might do the same.” Malhotra shares that “leaders who are committed to creating, supporting and funding affinity groups” is one of the best examples of practicing collaborative culture rather than an individualistic one where everyone is out for themselves.
- Create a workplace built on trust. We’ve depended on a command and control model for years—you, the employee, will do what I, the employer, want in the way that I want to get the results that I want. Leading with trust is still relatively rare in the workplace, which causes big problems for your workforce. “When trust is no longer present, we feel like we are walking on eggshells; we are always second-guessing our decisions and suspicious of the actions of others,” says Minda Harts in Talk to Me Nice: The Seven Trust Languages For a Better Workplace. Employees who feel this way are much more likely to be anxious and depressed, as well as to contribute to your growing re-hiring costs.
- Embrace the pause. Our workplaces are built to produce. And more always seems to be the answer. But there are major ramifications to the pressure to do more, produce more, sell more, help more. As a leader, you absolutely can do something to support a workplace less destined for burnt out employees: you can embrace the pause. Become comfortable with saying no to a new project if you know it will overburden your team. Pause non-critical projects or events and use that time to rebuild trust, build a collaborative culture, listen to your team members about what’s not working and co-create systems that make positive changes.
- Broaden who you’re learning from. When you start to seek out content from voices that are different than yours, you broaden your perspective, which makes you a much better leader. Taking time to self-educate from podcasts, books, interviews, TED talks, songs or more from voices you’re less familiar with will change how you interpret the world, and allow you to see more of what your team members might be feeling or responding to.
Entrepreneurship Section
The 5 Biggest Business Fails of 2025
By Sam Blum | Inc | December 17, 2025
3 key takeaways from the article
- Every year, a certain number of high-profile business failures blow up and become a point of fascination for the public.
- The best business failures provide lessons for entrepreneurs on how to do things differently. So this year—like every year—Inc magazine highlights the gaffes, miscues and fumbles that defined the business world and leant themselves to our trolling delight.
- Here are the five biggest business fails of 2025. A) McDonald’s’ AI-generated commercial “The Most Terrible Time of The Year.” It became a flashpoint of global derision against AI’s encroachment into everyday life, as it depicted AI-generated people dealing with various Christmas catastrophes instead of celebrations. B) Meta’s disastrous keynote at Meta Connect 2025 where company’s AI Rayban sunglasses promptly started to bomb. Zuckerberg’s had no clue what to do. C) Friend’s subway ads those ads were often defaced by New Yorkers who prefer real friends over synthetic ones. D) Poppi’s fraught influencer campaign. And E) Tesla’s Optimus robot’s downfall – given how Tesla chief Elon Musk had lavished the robot with hype, claiming it would somehow generate $10 trillion in revenue for the EV maker.
(Copyright lies with the publisher)
Topics: Entrepreneurship, Startups, Failure, Resielence
Extractive Summary of the Article | Read | Listen
Every year, a certain number of high-profile business failures blow up and become a point of fascination for the public. Sometimes, they involve founders going to jail (think Elizabeth Holmes, founder of Theranos, a fraudulent biomedical horrorshow) or founders being forced out of their companies (as happened to former WeWork CEO Adam Neumann). Then there are self-driving cars that have crashed and secret romances among colleagues that have been exposed to the world at a certain Coldplay concert.
The best business failures provide lessons for entrepreneurs on how to do things differently. So this year—like every year—we’re highlighting the gaffes, miscues and fumbles that defined the business world and leant themselves to our trolling delight. Here are the five biggest business fails of 2025 (in no particular order).
- McDonald’s’ AI-generated commercial. This month, McDonald’s aired an AI-generated commercial that was promptly spurned by seemingly every human who saw it. The ad, which aired in the Netherlands, was titled: “The Most Terrible Time of The Year.” It became a flashpoint of global derision against AI’s encroachment into everyday life, as it depicted AI-generated people dealing with various Christmas catastrophes, like Santa’s sleigh breaking down and holding up traffic, and cookie batter splattering all over a man’s face. Ultimately, the synthetic actors find refuge within the golden arches of McDonald’s. Online, people didn’t think the spot was very Christmasy, hurling the obvious allegations of “AI slop” at the burger conglomerate. McDonald’s Netherlands pulled the ad three days after it debuted. McDonald’s wasn’t the only corporation to air an AI-generated commercial and quickly feel repercussions from a bristling public. Coca-Cola did the exact same thing in November—for the second year in a row!
- Meta’s disastrous keynote. At Meta Connect 2025, the tech giant’s annual conference for developers, CEO Mark Zuckerberg took to the stage to introduce the company’s AI Rayban sunglasses and promptly started to bomb. Zuckerberg’s presentation was interrupted by a constant stream of tech snafus. His attempts to answer a call through the glasses were unsuccessful as the device just kept on ringing. And ringing. Until Zuck, visibly flummoxed, said to the audience: “I don’t know what to tell you guys.”
- Friend’s subway ads that made mostly enemies. The AI startup Friend definitely earned a foe in the average NYC commuter this year when its subway ads were defaced. Friend makes an AI companion device on a necklace that uses audio recordings as prompts for a chatbot in a corresponding app. The ads were simple, and offered a definition of the word “friend” written in black typeface against a white background. Those ads were often defaced by New Yorkers who prefer real friends over synthetic ones, and who scribbled criticisms such as “surveillance capitalism” across them. For Schiffman, the campaign was an all or nothing kind of gambit that cost around $1 million.
- Poppi’s fraught influencer campaign. Back in February, Poppi, the prebiotic soda brand, aired a high budget Super Bowl commercial featuring a gaggle of influencers. That probably would have been fine, but then the company sent Poppi vending machines to select influencers, which many would-be fans of the brand saw as a disingenuous marketing ploy, particularly because many of the influencers are already pretty well paid and have enough free soda. Many commenters on TikTok argued Poppi could have given vending machines to first responders or college students, for example.
- Tesla’s Optimus robot’s downfall. In a reminder that the future is definitely not now, Tesla’s humanoid robot Optimus had a really hard time standing at an October event in Miami. Clips of the robot losing its balance in front of a table of plastic water bottles went viral. The robot lifts its hands in a way that suggests it just can’t take it anymore, and seemingly gives up, falling backwards into the darkness. It was all the more funny, given how Tesla chief Elon Musk had lavished the robot with hype, claiming it would somehow generate $10 trillion in revenue for the EV maker. See below to understand what $10 trillion looks like crumpled up on the ground.
8 Creative Side Hustles We Discovered in 2025 — Which One Can Make You Money in 2026?
By Amanda Breen | edited by Jessica Thomas | Entrepreneur | December 16, 2025
2 key takeaways from the article
- The best side hustles aren’t just an opportunity to earn quick extra cash: They offer flexibility, fuel creativity and help people build income streams that can grow with them over time.
- Whether you’re hoping to offset rising costs, save for a significant goal or experiment with a new business idea, the right side hustle can spin the hours outside of your 9-5 into life-changing momentum. Entrepreneur sat down with dozens of side hustlers in 2025 to explore how they’re making money now and setting themselves up for success down the line. Eight of most promising side-husstles are: Tutoring, Creating content for brands, Reselling on TikTok live, Starting a podcast, Investing in domains, Creating a product, and selling a skill by hour.
(Copyright lies with the publisher)
Topics: Startups, Entrepreneur, Side-husstles
Extractive Summary of the Article | Read | Listen
The best side hustles aren’t just an opportunity to earn quick extra cash: They offer flexibility, fuel creativity and help people build income streams that can grow with them over time.
Whether you’re hoping to offset rising costs, save for a significant goal or experiment with a new business idea, the right side hustle can spin the hours outside of your 9-5 into life-changing momentum. Entrepreneur sat down with dozens of side hustlers in 2025 to explore how they’re making money now and setting themselves up for success down the line.
- Tutoring. If you’re an expert in subject matter that people want to learn, tutoring, whether online or in-person, can be a great way to side hustle your knowledge into additional income. Seattle, Washington-based tutor Carter Osborne started tutoring as a side hustle in 2017 to help with tuition payments while in graduate school. According to Osborne, “I am hardly the stereotype of a business owner: I studied public policy in college and never dreamed of starting a business. There’s no such thing as a ‘type’ of person who becomes a successful business owner, so go pursue your ideas and see what happens.”
- Creating content for brands. Have an eye for content that helps brands go viral and captures consumer interest? Plenty of brands invest in the service, known as user-generated content or UGC, and it could be your next side hustle.
- Reselling on TikTok live. If you have a passion for collectibles or another in-demand product that generates buzz, consider capitalizing on it with a resell side hustle in the spotlight — selling goods live on TikTok or another social media platform.
- Starting a podcast. This versatile side hustle doesn’t require getting in front of a camera to share your ideas with the world — just find a topic you enjoy talking about, line up a first guest or two, partner with like-minded advertisers who can help you earn revenue and press record.
- Investing in domains. Have a talent for intuiting which domain names might command a high price? Domain investing could be the flexible, virtual side hustle for you.
- Creating a product. Side hustles can be the perfect way for aspiring entrepreneurs to test-drive their businesses before going all-in, so if you have an idea for a product, it can pay to build it in your spare time.
- Selling skills by the hour. If you have a skill someone will pay for by the hour, all you need is your first customer to get your side hustle up and running. Platforms like Taskrabbit can be a great way to connect with people who want to pay for your services.
Leave a Reply
You must be logged in to post a comment.