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The world of popular psychological ideas, which is largely the self-help industry, is not short of contradictions. For instance, it simultaneously promotes the benefits of emotional intelligence (the ability to empathize with others and engage in strategic impression management) and authenticity (the tendency to express what you really feel and think without much consideration for others opinions). It also frequently celebrates self-acceptance and constant self-improvement (love yourself as you are but also become the best version of yourself), mindfulness and relentless ambition (stay in the zone, present and serene while hustling aggressively toward big goals), and even self-awareness and self-belief, which pull in opposite psychological directions. Self-awareness requires confronting your flaws, limitations, and blind spots with brutal honesty; self-belief requires ignoring at least some of that evidence to maintain high-levels of confidence, optimism, and drive. One asks you to see yourself clearly; the other asks you to believe in yourself despite what you see. Yet this isnt a logical flaw so much as a reflection of our human tendency to categorize things as either fully good or fully bad, when in reality most psychological qualities operate in a yinyang balance. As Aristotle argued in his doctrine of the golden mean, virtue itself sits at the midpoint between two vices courage between cowardice and recklessness, generosity between stinginess and extravagance, confidence between timidity and hubris. In other words, even the qualities we most admire become dysfunctional when taken too far, and even the traits we distrust can be valuable in moderation. Human behavior functions the same way: most psychological strengths arent inherently good or bad, theyre dose-dependent. In line, emotional intelligence isnt inherently superior to authenticity; self-awareness isnt automatically better than self-belief. They each contain the seed of their opposite, and their value depends on the situation, dosage, and context. In fact, one of the most established findings in personality and organizational psychology is the too-much-of-a-good-thing effect: virtually any trait or competency becomes dysfunctional when taken to an extreme. Confidence turns into arrogance, humility into self-doubt, authenticity into impulsive oversharing, and EQ into manipulative charm. Every strength has a shadow side, every virtue has a saturation point, and every desirable trait comes bundled with its own trade-offs. The goal, then, is not to pick one pure ideal authenticity or impression management, self-awareness or self-belief but to learn to calibrate them, blending them in ways that make us more effective, rather than more extreme. {"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/tcp-photo-syndey-16X9.jpg","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/tcp-photo-syndey-1x1-2.jpg","eyebrow":"","headline":"Get more insights from Tomas Chamorro-Premuzic","dek":"Dr. Tomas Chamorro-Premuzic is a professor of organizational psychology at UCL and Columbia University, and the co-founder of DeeperSignals. He has authored 15 books and over 250 scientific articles on the psychology of talent, leadership, AI, and entrepreneurship. ","subhed":"","description":"","ctaText":"Learn More","ctaUrl":"https:\/\/drtomas.com\/intro\/","theme":{"bg":"#2b2d30","text":"#ffffff","eyebrow":"#9aa2aa","subhed":"#ffffff","buttonBg":"#3b3f46","buttonHoverBg":"#3b3f46","buttonText":"#ffffff"},"imageDesktopId":91424798,"imageMobileId":91424800,"shareable":false,"slug":""}} Hidden drawbacks At times, even traits that seem to have no downside, such as self-awareness, come with hidden drawbacks. Intuitively, one would assume that we are generally better off knowing ourselves, understanding how others perceive us, and being aware of our strengths, limitations, biases, and blind spots. After all, entire leadership models, coaching programs, and HR philosophies rest on the idea that insight precedes improvement. If you dont know whats broken, how can you fix it? If you dont know how others experience you, how can you expect to lead them? And if you dont understand your own motives, how can you trust your decisions? To be sure, this intuition is backed by a substantial body of research. For example, many scientific studies show that: 1) Self-awareness predicts better job performance. Employees with higher self-insight (as measured through multisource or 360-degree feedback assessments) tend to show greater effectiveness at work, including when they are managers and leaders.2) Self-awareness enhances leadership effectiveness. Leaders who are more attuned to their strengths and weaknesses receive higher performance ratings and foster better team climates (note, however, that underestimating your skills and leadership talents is also linked to higher leadership effectiveness compared to people who overestimate themselves).3) Self-awareness improves interpersonal relationships. Individuals who understand their emotional patterns and their impact on others display higher empathy and lower conflict. Its simple: if you know how you impact others, which equates to knowing how others see you, it will be easier for you to adjust your behavior to make a desired impact on others (this is what David Brent and Michael Scott fail to do, which makes The Office great comedy value but their characters an absolute nightmare archetype of a boss). The value of selective ignorance However, there are also well-documented benefits to poor self-awareness or, more precisely, benefits to selective ignorance, including being unaware of your limitations or unjustifiably pleased with yourself. Think of people with the arrogance or confidence of Kanye West, Cristiano Ronaldo, or Muhammad Ali but without the talents to back it up! Consider the following findings: First, people with inflated self-views tend to be more resilient and less affected by stress, being able to bounce back faster and stronger from setbacks. Along the same lines, decades of research on positive illusions shows that overly optimistic people cope better with adversity and maintain higher motivation. Second, self-deception can make individuals more persuasive: people who genuinely believe they are more competent than they are often appear more confident and convincing to others. If you can fool yourself, you are much more likely to fool others, since you dont even have to pretend or lie. Third, low self-awareness can fuel ambition. Many entrepreneurs, athletes, and leaders overestimate their odds of success and this unrealistic optimism propels them to attempt things that a more accurate self-assessment would quickly veto. The worlds innovations are not driven by people with perfectly calibrated self-views, but by those who believed they could fly even when the evidence suggested otherwise. All of which is to say: the self-help promise of clean, linear psychological virtues overlooks how messy human functioning actually is. A bit like nutrition advice that alternates between demonizing carbs, demonizing fat, and demonizing sugar (sometimes all three, and at times none), the self-help world tends to spotlight traits in isolation, ignoring the context in which they operate. Authenticity is wonderful until its not. Confidence is powerful until it becomes delusion. Empathy is admirable until it becomes people-pleasing. Even mindfulness has a dark side when it becomes an excuse for avoidance or emotional disengagement. A tool box A more realistic (and scientifically grounded) way of thinking about psychological qualities is to view them as tools in a repertoire. A hammer is useful, but not if you treat every situation as a nail. Emotional intelligence is helpful, but not if it turns into strategic manipulation. Authenticity is refreshing, but not if it comes at the expense of tact, professionalism, or prosocial self-regulation. And self-awareness is enlightening, but not if it becomes rumination, self-criticism, or paralysis by analysis. The true art of psychological competence, especially in leadership, is not picking the right trait but deploying the right trait at the right time. Its knowing when to believe in yourself fiercely, and when to question your assumptions. When to be transparent, and when to filter. When to push ruthlessly, and when to pause reflectively. When to take a risk, and when to seek feedback. Most importantly, its recognizing that every psychological asset becomes a liability when unbounded, and every liability contains the seed of an asset when calibrated properly. If the self-help industry were more honest, it would sound far less like a collection of tidy commandments and far more like a user manual for a complex operating system: one with settings, thresholds, sliders, and context-specific modes. But it depends will never be a bestseller, and everything in moderation is hardly a motivational tagline. So instead, we get a contradictory buffet of directives be yourself, but improve yourself; relax, but hustle; speak your truth, but avoid offending anyone; know your flaws, but never doubt your greatness. The irony, of course, is that mature psychological functioning lies precisely in reconciling these tensions. Not by choosing sides, but by developing the agility to move fluidly between them. In the end, the real contradiction is not in the advice we receive, but in our desire for simple answers to complex questions. Human nature is too nuanced for single-variable solutions, and the qualities that make us effective are rarely pure. They are contradictions held in balance (the yin and yang of psychological functioning) and the leaders who thrive are those who learn to navigate this paradox elegantly, not dogmatically. {"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/tcp-photo-syndey-16X9.jpg","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/tcp-photo-syndey-1x1-2.jpg","eyebrow":"","headline":"Get more insights from Tomas Chamorro-Premuzic","dek":"Dr. Tomas Chamorro-Premuzic is a professor of organizational psychology at UCL and Columbia University, and the co-founder of DeeperSignals. He has authored 15 books and over 250 scientific articles on the psychology of talent, leadership, AI, and entrepreneurship. 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The rise of artificial intelligence is transforming every industry, but it also creates enormous demand for digital infrastructure and natural resources. Data centers, the engines of this transformation, consume vast amounts of water and energy. A single hyperscale data center consumes up to 5 million gallons of potable water every day. In Phoenix, 58 centers together demand more than 170 million gallons daily, enough to serve up to several hundred thousand households. This is the internets hidden water footprint, amplified by AI, cloud computing, and data-heavy services. Training a single large AI model in a Microsoft data center can require about 185,000 gallons of clean water. By 2027, AI-related data centers could consume 1.7 trillion gallons annually, nearly matching the domestic water use of some developed nations. Most data centers still rely on evaporative cooling, which consumes massive volumes and discharges chemical-laden wastewater. The challenge is not only scale but also geography. More than 40% of U.S. data centers are located in water-stressed basins. AIs rapid growth demands a new approach. Water cannot become the bottleneck to the next chapter of human progress. FROM COMMUNITY PUSHBACK TO BUSINESS RISK Public concerns are already reshaping the industry: Oregon: Google faced lawsuits over water secrecy. Indiana: Amazons Project Rainier is under state scrutiny for allegedly draining wells while pumping millions of gallons per hour. Georgia: Families near Metas complex report unusable wells. Virginia: Utilities now require new data centers to secure their own water sources or adopt closed-loop systems. Investors are paying attention. Water use per AI training cycle is emerging as a core accountability metric, alongside carbon intensity. Communities are responding with moratorium requests. THE CALL FOR INDUSTRY LEADERSHIP The industry can no longer rely on incomplete data, inconsistent reporting, or distant offsetting schemes. Declaring a water positive target by some far-off date is no longer enough. Communities demand tangible action where the water is drawn. The technology exists today. Around the world, data center and cloud providers are proving that sustainability and scalability can coexist, with each breakthrough setting a new benchmark for what is possible. Microsoft has deployed closed-loop systems in Arizona and Wisconsin, saving up to 125 million liters per site. Google used reclaimed wastewater at 22% of its campuses as of 2023. Amazon is building new centers with closed-loop treatment, recycling every drop used for cooling. NVIDIA is partnering with Singtel to deploy next-generation liquid and immersion cooling systems designed to achieve industry-leading water efficiency in Singapores new AI data centers. The opportunity is clear: Water must be engineered into AIs growth, not treated as an afterthought. THE TECHNOLOGIES DRIVING SUSTAINABLE AI Building a sustainable digital future requires bold adoption of both proven and emerging solutions that reduce environmental impact while enabling continued growth. The tools already exist. What we need now is the conviction to scale them. Smarter cooling technologies Closed-loop and liquid cooling: Advanced systems can reduce water consumption by as much as 30 to 50% while maintaining the high-performance environment that AI workloads demand. Water recycling at scale: Leaders like AWS plan to deploy treated wastewater at more than 120 data centers by 2030, setting a new baseline for responsible water use. AI-Driven optimization Smart workload scheduling: By applying AI to manage computing loads, operators have shown they can cut water consumption by a third without increasing carbon emissions. This type of efficiency breakthrough makes sustainability scalable. Alternative water sources Seawater desalination: In coastal or arid regions, seawater offers an abundant alternative. Advanced desalination technologies convert it into a reliable cooling supply without burdening municipal drinking water systems. High-value water reuse: Modern treatment technologies can transform sewage, brackish groundwater, and industrial effluent into high-quality process water, eliminating dependence on limited freshwater supplies. This is our approach at Gradiant, where our feedwater-agnostic treatment systems enable data centers to operate using seawater, wastewater, or other unconventional sources, reducing dependence on fresh supplies. By recycling blowdown and cooling tower reject, we achieve zero-liquid discharge and drastically reduce freshwater withdrawals, even in the largest hyperscale AI facilities. With the right technologies, sustainable AI data center growth can align with both environmental and business imperatives. AIS GROWTH HINGES ON WATER The next era of AI will be defined by those who treat water as critical infrastructure. Companies that lead will gain faster permitting, avoid regulatory shocks and operational disruptions, and build lasting trust with the communities that host them. Water is not compliance. It is resilience. It is innovation. It is license to operate. AIs future depends on leadership that recognizes water as the defining resource of our digital age, one that must be safeguarded through innovation rather than depletion. Advanced recycling, seawater desalination, and next-generation water treatment will be the pillars of responsible growth. The companies that act now will determine not only how AI grows but whether it grows responsibly, securing both digital progress and planetary resilience. Prakash Govindan and Anurag Bajpayee are the cofounders of Gradiant.
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The Bronx stands apart from New York Citys four other boroughs in stark ways. Home to 1.4 million residents and the nation’s poorest congressional district, it once flourished as fertile farmland. Today, were restoring this landnot to its agricultural roots, but as fertile ground for raising healthy, happy, and prosperous children. And in the process, were cultivating opportunity for a new generation of citizens. My wife Lizette and I founded and run Green Bronx Machine (GBM). Our nonprofit is dedicated to rewriting the narrative about the Bronx and its residents. Inside Community School 55, just across the tracks from rows of dilapidated public housing towers, sits an unexpected oasis: a thriving garden where fruits and vegetables grow alongside young dreams and possibilities. All year long, grandmothers find respite in the greenery while children eagerly plant seeds, harvest crops, raise chickens, and gather eggs. But this transformation didn’t begin outdoorsit started in a classroom. AN “UNEMPLOYED” TEACHER I playfully call myself an “unemployed teacher.” An educator/administrator since 1984, I left formal employment determined to launch a program that has now spread to more than 1,000 schools across the United States and a dozen countrieswith ambitious plans to scale that impact. Dubbed A Miracle in the Bronx, we combine urban agriculture, project-based learning, and community engagement that transforms educational outcomes in areas where success seems improbable, if not impossible. GBMs classroom model began almost by accident. When struggling to engage my students, I received a box of daffodil bulbs. Instead of discarding them, I tucked them behind a radiator. Weeks later, the bulbs sprouted and bloomed, and with them, a change in students’ engagement and attendance. These kids, who wouldn’t come to school to see me, were suddenly showing up to see plants. That was my a-ha moment. We planted 25,000 bulbs all across NYC that year. [Photo: Green Bronx Machine] Today, the program features indoor Tower Gardens and Babylon Micro-Farms, where students grow vegetables year-round in classroom settings, along the way learning math, English, biology, even phys. ed. The results extend far beyond agriculture. Participants show improved academic performance, higher attendance rates, better nutritional habits, and increased environmental awareness. Teachers are similarly inspired and engaged. Meanwhile, the produce students grow is sold to provide much-needed jobs and income, or taken home by students to feed their families. I learned that when a child plants a seed and nurtures that plant to harvest, they never go hungry againnot intellectually, emotionally, or physically. THE VISION DEFICIT IN AMERICAN SCHOOLS It is common to think that America’s educational challenges stem primarily from limited funding. But the more fundamental issue is a clear vision of whats possible in todays schoolssomething increasingly scarce in an environment dominated by misinformation, politics, and eroding social cohesion. For children growing up today, the harsh reality is that in America, despite our cherished narrative of meritocracy and individualism, ones ZIP code remains the primary determinant of social, educational, and health outcomes. Thats exemplified in marginalized areas like the South Bronx. This geographical determinism is driven by many things. That includes schools in low-income areas being starved for funding, experienced teachers, and enrichment opportunities. Students also face additional barriers such as food insecurity, housing instability, and exposure to environmental hazardsall impacting their ability to learn effectively. END ZIP CODE DESTINY By transforming schools into centers of community wellness, individual excellence, and environmental stewardship, weve demonstrated that innovative approaches can overcome systemic barriers. We’re growing high performing schools, engaged citizens, responsible neighbors, vibrant communities, jobs, and we’re growing healthy foodall together. The program has driven impact across a wide variety of communities, national and international, and that impact is captured in a documentary, Generation Growth, which highlights the program’s success and led to GBM being named a 2024 Most Innovative Company by Fast Company. SCALE A TRANSFERABLE MODEL What makes GBMs method so impactful is its transferability across states and international borders. Schools in diverse settings, from rural Alabama to suburban Colorado, have successfully adapted it to local needs while maintaining core principles. Were projected to impact 30,000 schools in the United States by 2030. This isn’t just about the Bronx. There is a Bronx in every American city and around the world; weve built a turn-key program that serves all of them. This is about transforming how we think about education, community, sustainability, poverty, and progress everywhere. [Photo: Green Bronx Machine] Many think I have a larger-than-life personality, but you dont need that to be effective. Its about community engagement. Ana Christina Garcia of Sloan Kettering and a GBM board member notes that “Green Bronx Machine capitalizes on community assets and unlocks the potential, desire, and passion that children, principals, and teachers already have. Community engagement is about making organizational resources more accessible to unlock people’s existing talents and power. It’s a two-way street where everyone benefits from sharing their wonderful talents as human beings and creating stronger community connections.” I call this social vitamin fortified with human capacity. We’re not just growing plants, we’re growing hope. And hope is the most powerful seed we can plant. In 2026 Id like to shake hands with other thought leaders to continue bringing this proven program across the country. It takes a village, of course, but it also takes an inspiring vision. Join me please. The author thanks Joel Makower and Jeff Senne for their contributions to this article. Stephen Ritz is founder of Green Bronx Machine.
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Ralph Lauren revealed Team USA’s Milan Cortina Winter Olympics looks Thursday, complete with Americana knit sweaters and plenty of vintage call-backs.The formal opening ceremony look pairs a patterned red, white and blue knit sweater with tailored cream trousers and a matching wool coat. Moving sportier, the closing ceremony outfit features a graphic puffer coat inspired by vintage ski kits over a color-blocked sweater.“We are creating something that we know has to become timeless and has to be something that people will wear forever and appreciate forever,” said David Lauren, the Chief Branding and Innovation Officer at Ralph Lauren. “So in creating jackets like this and creating things, we’re looking at the things that we most cherish. Things that are already enduring parts of the Ralph Lauren lexicon, and then we’ll build on that.”Beyond the ceremony looks, a Team USA collection, which will also be given to athletes as Olympic village wear, became available to public Thursday.The collection follows similar design themes as the opening and closing ceremony looks, with classic red, white and blue patterning on lots of knits, and includes Ralph Lauren’s versions of winter staples like bomber jackets and hockey jerseys.The process of creating these looks is a long one. The Ralph Lauren team, which has been designing Team USA’s Olympic apparel since 2008, starts on each Olympics’ looks about 2 1/2 years out from the Games, meeting with athletes and brainstorming ideas for the kits. As Milan-Cortina’s looks are unveiled, Lauren said the looks for the 2028 Los Angeles games are already months in the making.He knows the cultural importance each Olympics’ outfits holds, and the attention they garner in the fashion world and among American consumers.“The fact that we know people will want them and collect them and chase them down across eBay, is just an exciting part of the game,” he said.Sometimes, even international Olympic athletes are on the lookout for them. Beyond being an addition to an American athlete’s Olympic wardrobe, the pieces are also sometimes used as bargaining tokens in the Olympic village.Para snowboarder Brenna Huckaby and snowboarder Red Gerard explained to The Associated Press that there’s a tradition of swapping team sweaters and jackets with other nations at the Olympics, if there’s a certain country’s design that catches an athlete’s eye. That’s only if there’s a piece of their collection that they’re willing to let go of, that is.“I rarely trade, because I almost always love every single piece of Team USA stuff,” said Huckaby, modeling the color-blocked closing ceremony sweater that she said “is going to be on rotation after.”“But every now and then there will be some random thing that another country has. And it’s so hard to sit with all my bags, all my stuff open, like, ‘OK, what am I willing to part with?’ That is probably, aside from competing, the hardest part of the Games,” she said. AP Olympics: https://apnews.com/hub/milan-cortina-2026-winter-olympics Alyce Brown, AP Sports Writer
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Welcome to AI Decoded, Fast Companys weekly newsletter that breaks down the most important news in the world of AI. Im Mark Sullivan, a senior writer at Fast Company, covering emerging tech, AI, and tech policy. This week, Im focusing on the increasing pressure on the AI industrys wunderkind, OpenAI. I also look at the change in AI leadership at Apple, and at the music industrys new cooperation with AI music generation apps. Sign up to receive this newsletter every week via email here. And if you have comments on this issue and/or ideas for future ones, drop me a line at sullivan@fastcompany.com, and follow me on X (formerly Twitter) @thesullivan. Is OpenAI still the king? The AI industry has always been very competitive, and its getting even more so. A relatively small group of AI labs are slugging it out to release the smartest models, and, by extension, the smartest chatbots. Ever since OpenAI released its ChatGPT chatbot three years ago, the upstart company has been seen as the leader, but that status has been called into serious question by Googles new Gemini 3 Pro model (and the Gemini app).ChatGPT has grown quickly. The official number is 800 million weekly active users. Googles number is 650 million monthly active users for the Gemini chatbot. So, apples and oranges. SimilarWeb provides a somewhat better comparison, saying that Geminis share of web traffic grew from 5.7% a year ago to more than 15% today. Meanwhile, ChatGPTs 87% share a year ago shrunk to 71.3% today. OpenAI is feeling the pressure from Gemini (and probably from Anthropics new Claude Opus 4.5 model). CEO Sam Altman sent a memo to staff Monday declaring a “code red” effort to improve ChatGPT, according to The Information and other outlets. The effort includes reducing investments in enhancing the health information available on ChatGPT, as well as reducing work on the shopping experience, and the advertising that could go around that. “Our focus now is to keep making ChatGPT more capable, continue growing, and expand access around the worldwhile making it feel even more intuitive and personal,” ChatGPT product lead Nick Turley tweeted Monday. In a wider sense, OpenAI is losing billions, and spending billions, a fact that must make its investors both nervous and curious. Leaked documents and analyst estimates show OpenAI will lose between $9 billion and $11 billion in 2025 (spending roughly $22 billion while bringing in about $13 billion in revenue). The company recently told investors that its spending through 2029 could rise to $115 billion. Altman has said his company, partners, and investors will commit as much as $1.4 trillion to infrastructure (chips, data centers, etc.) in the next eight years. OpenAI is an aggregator, as the analyst Ben Thompson points out. The fact that its willing to de-emphasize its shopping and advertising experiences, which are potential revenue generators, shows that its still in the mode of growing users, and not yet in the mode of growing revenue. And the way that aggregators (like Facebook) grow is by becoming more things to more people in order to maximize attention and engagement on its platform, regardless of whether the users are paid subscribers. In the aggregator model, actually monetizing all those eyeballs comes later. The confidence in that model, which requires constant growth toward a critical mass of users, has afforded OpenAI a certain swagger, and even a cavalier attitude about making returns for its investors. One of those investors, Altimeter Capitals Brad Gerstner, asked Sam Altman during an October podcast (12:30 mark) how he explains to the markets spending more than a trillion on infrastructure when his company is operating deep in the red. Altman was exasperated. Brad, if you want to sell your shares, Ill find you a buyer, he said. I just . . . enough. But it’s no longer clear that OpenAI has the best models and the go-to chatbot. Setting aside the shopping and advertising work, OpenAI is right to reassign its talent to work on new models and new skills for ChatGPT. This also might mean taking talent off fun projects like the Sora app, which seems far afield from the mission of making ChatGPT the highest performing chatbot available. On the other hand, things can change very quickly in the AI world. Reports say OpenAI is already set to release a new reasoning model codenamed Garlic that will overtake Gemini 3 on a number of key benchmarks. Well see if Garlic gets a better reception than GPT-5. Apple must keep publishing AI research under Subramanya This week Apple announced that its AI boss, John Giannandrea, will be leaving the company. Giannandrea had been a successful AI leader at Google, but his name is linked to Apples failure to seize on generative AI to improve its Siri voice assistant and make the iPhone and other iDevices smarter and more personalized. Hell be handing the reins to another Google vet, Amar Subramanya, who once led engineering on Googles Gemini chatbot, and is stepping down after seven years on the job. Apples stock price got a slight boost on the news, as some investors saw Apple signaling a new urgency to bring AI to its devices. Subramanyas remit will be restoring Apple to some kind of parity with its peers in developing AI models and applying them in meaningful ways. As Mark Zuckerberg can attest, achieving that goal will depend on recruiting and retaining top-shelf AI researchers. Giannandreas AI/ML group saw a lot of churn and lost a number of top shelf researchers to Meta and others, including Ruoming Pang and Robby Walker. One reason for this was the groups habit of investing time and labor in technical approaches to problems only to see them scrapped. Another was the slow pace of developing and releasing new AI features for products like Siri. Another problem is publishing. Apple is famously secretive about its R&D in all areas of the company. The company likes to talk about customer-facing products, and dislikes talking publicly about the technology that makes them work. AI researchers arent OK with that. They want to publish their research. They want the exposure and influence that can bring within an ultra-competitive industry. When Giannandrea came to Apple, the company began allowing its AI talent to publish more of their researchto the extent they could do so without revealing trade secrets. Apple now has a Apple Machine Learning Research web page that lists published papers, technical reports, and conference submissions. It will be crucial that Subramanya keeps this practice going, or expands it. Otherwis Apple risks losing key researchers to competitors. Record Labels are having their iTunes moment with AI The Music Industry has stopped suingAI music generation appsinstead, its making deals with them: The three major record labels have now signed licensing agreements with AI music startups. Warner Music Group, Universal Music Group, and Sony Music Entertainment have made licensing deals with an AI music startup called Klay Vision. The agreements grant Klay Vision permission to train its music generation models on music catalogs owned by the labels, replacing previous models that relied on scraped or unauthorized data. AI-generated music is getting more popular. An AI-generated song using a simulation of a real human country singers voice recently hit number one on the Billboard Country Digital Song Sales ranking. Suno, another AI music company that previously faced lawsuits from major labels, has signed what it calls a “first-of-its-kind partnership” with Warner Music. The deal moves the company toward licensed, artist-opt-in AI models. The moment feels similar to the record labels decision in the early 2000s to sell digital music on Apples iTunes platform. The labels saw CD sales tank as consumers downloaded free MP3s from sites like Napster and Limewire. More AI coverage from Fast Company: The Trump administration keeps taking stakes in chipmakers it may come back to haunt them Will chatbots ever be funny? Why these comedians arent worried about an AI takeover, yet Can your AI adapt to multiple cultures? 10 ways I use AI to be a better journalist Want exclusive reporting and trend analysis on technology, business innovation, future of work, and design? Sign up for Fast Company Premium.
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