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  • Desvendando o Mito da Multitarefa e Resgatando a Produtividade Feminina por Marcelle Paiva

    Em um mundo que exalta a figura da "supermulher e guerreira”, a capacidade de realizar diversas tarefas simultaneamente é frequentemente vista como símbolo de honra ao mérito, “como aquela estrelinha no caderno de antigamente”. A imagem da executiva que, com graça e eficiência, balanceia carreira, maternidade e vida pessoal, e social, parece os símbolos do sucesso moderno. Mas essa persistente crença na multitarefa como sinônimo de eficiência pode, na realidade, estar prejudicando nossa produtividade e bem-estar. Durante anos, celebrei a minha própria habilidade de fazer malabarismos com inúmeros compromissos, até que a exaustão e questões pessoais me mostrou a falha nessa percepção. O orgulho de ser uma "alta performance da multi-tarefa" escondeu um custo emocional e uma falta de rotinas de bem-estar que só se revelou com o tempo. Esta mesma revelação está sendo ecoada em pesquisas científicas atuais, que desafiam o conceito de que as mulheres são naturalmente melhores na multitarefa do que os homens. Conforme explica um artigo da revista de neurociência da Universidade Stanford [Why multitasking does more harm than good], quando realizamos uma tarefa, várias redes cerebrais entram em operação. Fazer várias tarefas ao mesmo tempo, portanto, significa interromper a comunicação entre essas redes, o que pode significar o processamento da informação mais lentamente, além de cometer mais erros. Outra pesquisa publicada na revista Nature [Memory failure predicted by attention lapsing and media multitasking] aponta que a multitarefa intensa está associada aos lapsos de atenção e esquecimentos. De fato, o pesquisador Kevin P. Madore explica como alguns especialistas destacam que a multitarefa diária crônica “está relacionada a erros em nossa capacidade de reter e usar informações (a chamada memória do trabalho) e nossa capacidade de recuperar informações (memória a longo prazo) O neurocientista Daniel J. Levitin é mais claro a esse respeito. Conforme escreveu no The Guardian, “a multitarefa aumenta a produção de cortisol, o hormônio do estresse, bem como a adrenalina, o hormônio de luta ou fuga, que pode superestimular o cérebro e causar confusão mental ou pensamentos confusos. Os estudos citados são evidências cruciais que suportam a noção de que a multitarefa pode não ser o o simbolo de sucesso e eficácia que imaginamos. Destacando essas pesquisas, podemos argumentar que o mito da multitarefa não só sobrecarrega as mulheres com expectativas irreais, mas também negligencia o fato de que a verdadeira eficiência pode ser encontrada em uma abordagem mais focada e menos frenética à produtividade. Explorar pesquisas, estudos e livros, pode nos ajudar a entender as repercussões neurocognitivas do multitasking crônico, enquanto é claro, já sabemos isto nos exemplos da vida real de forma prática, que nos leva a uma menor qualidade de vida, a um aumento dos níveis de ansiedade e depressão e sem contar o stress para manter este esteriótipo, de que sim damos conta de tudo e somos guerreiras. Neste mês das mulheres, proponho rejeitarmos a capa da mulher maravilha e ao abraçar uma abordagem mais intencional para a gestão de tempo e energia, podemos não só melhorar nossa produtividade em até 40%, segundo o mas também nossa qualidade de vida. A verdadeira força reside em reconhecer nossos limites, estabelecer fronteiras saudáveis, praticar o autocuidado e sim pedir ajuda e dividir tarefas. Após eu reconhecer que deveria me transformar, em meados de 2019/2020, passei incluir intenção todas as manhãs, sim, precisa ser u exercício diário, ao despestar às 5h00 da manhã, começo com exercício gratidão e oração, alguma leitura que me deixa positiva, praticas do Yôga, para me manter em estado de presença e só depois começo a ter rotina com meus filhos e só depois começo no trabalho no escritório, antigamente era acordar, levar filhos na escola e escritório. É claro, que não conseguimos de uma hora para outra abandonar certos vícios de comportamento, como querer dar conta de uma de uma vez só e no mesmo minuto, então é respirar, estar consciente e ir cultivando o foco, por exemplo escrevi este artigo no 1º dia em que decidi me afastar das redes sociais por 20 dias, pois percebi que está atrapalhando minha produtividade e foco e me tornando ansiosa, nada é uma regra é ir cultivando, logo em volto, quando eu me senti mais focada para isto. Então, pare e refletiva sobre a sua rotina, histórias e sua força de reconhecer seus limites, e vamos começar uma nova narrativa neste mês da mulher. Uma narrativa na qual as mulheres tenham a liberdade de trabalhar de forma inteligente, não dura, e onde a produtividade seja medida por resultados e não pela habilidade de fazer malabarismos em quantidade em diversos temas ao mesmo. Menos julgamento, mais acolhimento e produtividade. MARCELLE PAIVA Eu me defino como uma aprendiz, que tem a alta performance como combustível e que se importa verdadeiramente com o tema saúde mental. Sou Bacharel em Propaganda e Marketing, MBA em Administração & Negócios pela ESPM e uma pós-Graduação em Gestão das Emoções nas Organizações no Hospital Albert Einstein e uma certificação de Conselheira Digital pela HSM. Atuo na área de Tecnologia da Informação há 23 anos, com uma trajetória nas maiores empresas do setor como TOTVS, SAP e ORACLE, nas áreas Marketing, Operações, Growth, e atualmente como Vice Presidente de Desenvolvimento de Negócios na Oracle. Também sou professora de inteligência emocional, programação para não programadores e liderança na Escola Conquer. Sou co-autora do livro Mulheres em Tecnologia e co-autora do livro “Gestão & Inovação em Negócios.  Sou voluntária no Instituto Proa TECH, para incentivar jovens em situação de vulnerabilidade e Gerando Falcões. Tenho artigos publicado na revista do MIT BRASIL, humanos e tecnologia. Sou mãe de dois garotos incríveis, apaixonada por esportes e praticamente de bike, corrida e yoga.

  • Dicas Culturais - Parte 3

    Enquanto trabalhava em um projeto envolvendo software de reconhecimento facial, Joy Buolamwini, pesquisadora do Media Lab do MIT, descobriu que o algoritmo não conseguia detectar seu rosto, até que ela colocou uma máscara branca. Joy descobriu que a maioria dos programas de inteligência artificial são treinados para identificar padrões baseados em conjuntos de dados que tem viés para pele clara e sexo masculino. "Coded Bias" (Netflix) é um documentário que explora como algoritmos de IA, onipresentes em publicidade, contratação, serviços financeiros, policiamento e muitos outros campos, podem perpetuar as desigualdades de raça, classe e gênero existentes na sociedade.

  • THE SHIFT - Sua identidade a Perigo com a IA. Como retomar o controle?

    Nossa privacidade e identidade digital estão cada vez mais a perigo, nesse mundo povoado por aplicações de IA. A tecnologia é chave para retomar o controle e proteger pessoas contra fraudes. Yasodara Córdova, pesquisadora-chefe em privacidade e identidade digital na Único IDTech, explica como fazer. Ouça aqui: https://theshift.info/talk/the-shift-199-sua-identidade-a-perigo-com-a-ia-como-retomar-o-controle/?utm_source=The+Shift+Newsletter&utm_campaign=0476e8b9ab-EMAIL_CAMPAIGN_2024_03_01_10_29&utm_medium=email&utm_term=0_-0476e8b9ab-%5BLIST_EMAIL_ID%5D INSIGHTS A página no LinkedIn de Yasodara Cordova O link para a palestra de Yasodara na SXSW 2024 O livro “The Paper Menagerie and Other Stories“, de Ken Liu O artigo “Chips to compute with encrypted data are coming“, sobre criptografia homomórfica O livro “Burn Book: A Tech Love Story“, de Kara Swisher O livro “Filterworld: How Algorithms Flattened Culture“, de Kyle Chayka A série “Westworld“, da HBO, no Max.

  • From Potential to Profit with GenAI - BCG AI Radar

    Font: Here By: Jessica Apotheker, Sylvain Duranton, Vladimir Lukic, Nicolas de Bellefonds, Sesh Iyer, Olivier Bouffault, and Romain de Laubier. BCG’s survey of 1,400+ C-suite executives reveals that GenAI is quickly changing the way companies do business—but 90% of leaders are still waiting for it to move beyond the hype or pursuing limited experimentation. 2023 marked a turning point for AI. ChatGPT, OpenAI’s wildly popular chatbot, became one of the fastest-growing web platforms of all time and now receives 1.5 billion visits every month. Bing Chat, Bard, and other generative AI (GenAI) competitors quickly followed. While almost all executives now rank AI and GenAI as a top-three tech priority for 2024, 66% of leaders are ambivalent or dissatisfied with their progress on AI and GenAI—and only 6% have begun upskilling in a meaningful way. How can executives set up their organizations to seize the business opportunities that GenAI offers? To answer this question, we surveyed 1,400+ C-suite executives in 50 markets and across 14 industries. Their responses provided us with a number of insights. AI Is a Top Strategic Priority AI has shot to the top of the executive agenda. GenAI has ushered in a new world of business opportunities—and executives are eager to capitalize on them: 71% of the leaders we surveyed say that they plan to increase their company’s tech investments in 2024, up from 60% in 2023, and an even larger percentage (85%) say that they will increase their spending on AI and GenAI in 2024. 89% of executives rank AI and GenAI as a top-three tech priority for 2024. 54% of leaders expect AI to deliver cost savings in 2024. Of those, roughly half anticipate cost savings in excess of 10%, primarily through productivity gains in operations, customer service, and IT. But Many Companies Are Falling Behind Although the uptick in investment is promising, most organizations aren’t doing enough to realize the full benefits of AI. The executives who expressed dissatisfaction with their organization’s progress on AI and GenAI highlighted several challenges, including a shortage of talent and skills (62%), unclear investment priorities (47%), and the absence of a strategy for responsible AI (42%): Only 6% of companies have managed to train more than 25% of their people on GenAI tools so far. 45% of leaders say that they don’t yet have guidance or restrictions on AI and GenAI use at work. In short, despite realizing the need to increase their investments in AI, too many organizations are slow to embrace the revolution. Consider this: two-thirds of the executives we surveyed believe that it will take at least two years for AI and GenAI to move beyond the hype, and 71% are focused on pursuing limited experimentation and small-scale pilots. Some 90% of leaders fall into one of these two categories. We call them observers. But this is not a time to wait and see. Winners recognize that GenAI is here to stay and that extraordinary opportunities for productivity gains—as well as topline growth—are within reach right now. What Sets the Winners Apart Our analysis, reinforced by several in-depth interviews of top executives, indicates that a relatively small percentage of companies are setting themselves up for success with AI—while others are falling perilously behind. Five characteristics set the winners apart. Winners invest in productivity and topline growth. Of the companies that expect to see cost savings from AI and GenAI in 2024, roughly half anticipate more than 10% in cost savings. In concrete terms, that would mean savings of $1 billion for a company with $10 billion in revenues. Organizations that plan to invest more than $50 million in AI/GenAI next year are 1.3 times as likely as their peers to expect cost savings in 2024—and 1.5 times as likely to anticipate more than 10% in cost savings. But the winners won’t just bank these savings. They’ll reinvest them in the business to create new revenue streams and drive further growth. This proactive approach to reinvestment should be every company’s North Star in its strategic planning. Winners are upskilling systematically. To reap the rewards of AI, leading companies ensure that teams know how to use it most effectively—and scale up their reskilling muscle accordingly. Most leaders agree that GenAI will create new roles in their companies and expect that, on average, almost half of their workforce will need to be reskilled in GenAI over the next three years. Winning companies already have a head start in adopting the necessary technologies. BCG's 2023 DAI study found that leading firms typically have three times as many full-time employees upskilled on AI as others do. Our survey yielded a similar finding: 21% of organizations spending upward of $50 million on AI and GenAI next year have already trained more than a quarter of their people on the relevant tools (versus just 6% of companies overall). Most executives, in fact, report that only 1% to 10% of their workers are currently trained on GenAI tools. Executives need training, too, as 59% of leaders surveyed say that they have limited or no confidence in their executive team’s proficiency in GenAI. Winners are vigilant about cost of use. With GenAI’s rapidly broadening accessibility, companies can expect swift adoption—and rising related costs—as use of these tools spreads. Currently, however, too few executives are thinking this far ahead. Only 19% of those surveyed view cost as the top concern when choosing an AI/GenAI solution. In organizations where GenAI is not well implemented, the potential run cost is huge, given the technology’s fast uptake. The associated costs will only grow as companies launch customized projects at scale. Executives must proactively manage usage costs if they don’t want to deal with an expensive surprise later on. Winners build strategic relationships. AI leaders understand that the technology and the solutions it makes possible are moving fast. Only 3% of executives consider preexisting partnerships a priority when looking for AI solutions. The winners are actively building a partnership ecosystem with multiple companies, including software providers and GenAI startups, in order to gain access to cutting-edge technology and create near-term value. Winners implement responsible AI (RAI) principles. The sheer speed of GenAI adoption makes RAI more important than ever—especially as threats in areas such as cybersecurity emerge. Organizations must be proactive in addressing RAI issues, no matter where they are on their AI journey. BCG research has shown that organizations whose CEOs participate in RAI initiatives realize 58% more business benefits than those whose CEOs are uninvolved. Of the companies in our survey that plan to invest more than $50 million in AI and GenAI in 2024, 27% put the CEO in charge of their RAI strategy (versus 14% overall). Looking Ahead Not long ago, only AI experts, data scientists, and machine learning engineers could implement AI. Now GenAI is instantly available to any employee who wants to experiment with it. Thierry Garnier, CEO of Kingfisher, describes how his company adapted to GenAI: “At the beginning, we restricted all access to ChatGPT and similar large-language models, and we had a lot of complaints. But you have to trust people and have clear principles. So we worked together across functions, including HR and IT, and gradually provided access, established rules, and offered mandatory training on best practices and dangers—and these principles allowed us to have a path forward.” The genie is out of the bottle. If 2023 was the year when AI became democratized, 2024 is the year to turn GenAI’s magic into business impact. We recommend three value plays to maximize GenAI’s potential: Deploy GenAI in everyday tasks to realize 10% to 20% productivity potential. Select and test GenAI tools, deliver massive upskilling, roll out solutions to support workers in day-to-day tasks, and carefully evaluate the costs of deployment. Reshape critical functions for 30% to 50% enhancement in efficiency and effectiveness. Anticipate the impact of GenAI on your workforce and core functions, create new roles, reallocate budgets, and guide a series of pilots that can reliably scale up. Invent new GenAI business models to build a long-term competitive advantage.Develop a strong customer-centric approach, and leverage first-party data and intellectual property to create interactions that customers can't find anywhere else. By pursuing opportunities in these areas, organizations can increase productivity, enhance efficiency and effectiveness, boost revenue, and build a long-term competitive advantage. The accompanying slideshow offers a detailed view of our survey results.

  • Your Organization Isn’t Designed to Work with GenAI

    Font: Here by: Paul Baier, David DeLallo, and John J. Sviokla Summary: Many companies are struggling to derive value from GenAI because of a fundamental flaw in their approach: They think of GenAI as a traditional form of automation rather than as an assistive agent that gets smarter — and makes humans smarter — over time. The authors suggest a framework, Design for Dialogue, for reimagining their processes to mirror the back-and-forth collaboration of human dynamics to create an effective and adaptable human–AI workflow. At the heart of the framework are three primary components: task analysis, interaction protocols, and feedback loops. Organizations are plowing millions of dollars into generative AI as they race to apply it in innovative ways ahead of the competition. Yet many are hitting roadblocks, not due to the known shortcomings of the technology, which is still in its infancy, but because of a fundamental flaw in their approach: They think of GenAI as a traditional form of automation rather than as an assistive agent that gets smarter — and makes humans smarter — over time. - The introduction of the internet, mobile computing, and cloud platforms showed us that extracting full value from groundbreaking technologies lies not in merely integrating them into existing business processes, but in completely reimagining those processes. While GenAI may prove even more transformative than these innovations, it similarly demands redesigning the way work gets done to maximize its potential. The problem companies are facing, however, is that traditional methods of process redesign may not be entirely up to the task because GenAI doesn’t function like a traditional technology. Users “talk” to it, much as they would to a human colleague, and it works with the user in an iterative fashion. It also can continuously improve as it learns user needs and behaviors (and vice versa). To effectively integrate GenAI, we propose a new paradigm: Designing for Dialogue. Unlike traditional, technology-driven process redesign principles that focus on taking capabilities “out” of the human and putting them “into” the machine, Designing for Dialogue is rooted in the idea that technology and humans can share responsibilities dynamically. Each takes the lead at different points throughout a process based on context and competence. It essentially treats GenAI more like a coworker than a static technology. Make no mistake: We aren’t suggesting that GenAI is human or sentient — only that it should be treated differently than other technologies because it behaves more like a colleague than previous software. By designing for dialogue, organizations can create a symbiotic relationship between humans and GenAI. The approach also provides the flexibility for the process to become more efficient over time, almost organically. And every more-efficient process that emerges can be captured and examined for potential future automation, putting organizations on a path to continuous improvements and efficiency gains. Why old reengineering methods won’t work Back in 1990 the Harvard Business Review published a breakthrough article by the late Michael Hammer: “Reengineering Work: Don’t Automate, Obliterate.” It laid out the fundamentals of business process reengineering (BPR), which emphasizes the rethinking and radical redesign of business processes to achieve significant improvements in efficiency, quality, service, and speed. The approach calls for dissecting and understanding the entirety of a business’s workflows and reconstructing them from the ground up for optimal performance. When performed to take advantage of a new technology, BPR is typically led jointly by IT and the business. A working team assigns tasks previously performed by humans to the new technology based on the technology’s fixed capabilities. The team redefines the workflow around these static automations and, likely with significant change management, employees adopt the redesigned process. Ideally, the implementation team then monitors the new process, tweaking it periodically for continuous improvement, but not undertaking frequent, wholesale redesigns. The BPR method has worked for businesses for decades and is still predominantly employed in digital transformations today. While BPR’s goals and task-based philosophy can certainly apply to GenAI, its top-down approach aimed at both rigidly assigning tasks to either humans or technology and creating a fixed process fails to capitalize on GenAI’s flexible, iterative nature. Unlike previous technologies, GenAI facilitates a dynamic interaction and bidirectional feedback loop between human and machine. In other words, the AI and human perform a task together, learn from and improve each other, and continuously optimize a process at the user level in near-real-time. When integrating GenAI, the implementation team, therefore, becomes more of a facilitation team. A better way: Designing for Dialogue When we watch a high-performing human team in action, we see a dynamic collaboration: A project manager delegates tasks, but team leadership fluidly changes hands based on who can best address the challenge of the moment. Spontaneous brainstorming sessions lead to innovative solutions. Teammates seek counsel from each other for their unique areas of expertise, building up each other’s’ knowledge — and team performance — over time. The Design for Dialogue framework mirrors these human dynamics to create an effective, collaborative, and adaptable human–AI workflow. At the heart of the framework are three primary components: task analysis, interaction protocols, and feedback loops. A rigorous task analysis, akin to a project manager’s assessment of who is best suited for a particular role within a team, involves breaking down a process into its component tasks and evaluating the complexity, risks, and capabilities required for each. This ensures that each task is assigned to the right leader — AI or human. Interaction protocols — the equivalent of team ground rules — outline how AI and humans communicate and collaborate rather than establish a fixed process. One example would be when AI encounters an anomaly or a strategic decision point, it signals for human judgment, much like a team member would seek advice when faced with a hurdle. Another could be that proactive suggestions from AI serve as starting points for discussion, inviting human input to refine the course of action, rather than a steadfast command. Finally, just as teams debrief and adjust their approach in daily standup calls, or at least at each stage of a project, organizations will want to put mechanisms in place to continuously assess and fine-tune AI–human collaboration based on feedback. These could include error reporting, success metrics, user satisfaction surveys, and AI decision-making explanations. A good example is the customer service model employed by Jerry, a company valued at $450 million with over five million customers that serves as a one stop-shop for car owners to get insurance and financing. Jerry receives over 200,000 messages a month from customers. With such high volume, the company struggled to respond to customer queries within 24 hours, let alone minutes or seconds. By installing their GenAI solution in May 2023, they moved from having humans in the lead in the entirety of the customer service process and answering only 54% of customer inquiries within 24 hours or less to having AI in the lead 100% of the time and answering over 96% of inquiries within 30 seconds by June 2023. They project $4 million in annual savings from this transformation. To achieve it, they broke down the customer service agent’s role into knowledge domains — for example, quoting, binding and payments for insurance — and tasks, such as fielding an initial inquiry, assessing the nature of the inquiry, pulling up the correct source of information to find an answer, finding the specific user’s information, and so on. They discovered that while AI can take the lead in performing much of this work, there are points in the AI–customer interaction when matters need to be escalated to the agent, who then takes the lead. Interaction protocols determine when that should occur; for example, if AI detects negative sentiment from the customer, the AI can’t parse what the customer needs from their inputs into the chat, or the AI determines it can’t confidently provide an answer. Feedback loops are critical and used to refine the customer service process on a near-daily basis. Agents have been trained to identify issues and enter tickets into a tracking system, customers can click a thumbs up or thumbs down on an interaction, and instances of escalation are automatically flagged by the system for investigation. Engineers review failures daily and adjust the system to correct them. In addition to improving customer service and satisfaction, they’ve increased job satisfaction among human agents because AI conducts the laborious hunt for information regarding escalated issues and presents the agent with context and a clear course for action. Agents can also query the system directly to easily find additional information. And thanks to Jerry’s continued growth, they haven’t decreased the number of agents. Plus, the rich data Jerry collects from customer interactions translate into ways to improve other aspects of their business, such as by offering additional services aligned to customer needs and more targeted marketing and upselling. We’ve also used the Design for Dialogue approach at our research firm for the process of crafting reports. We broke down the process into its individual tasks and determined whether AI or a human takes the lead for each. Humans take the lead for tasks requiring human judgment, such as determining research focus and verifying information. We’ve established interaction protocols, such as requiring analysts to verify sources for any information provided by AI. And we’ve gathered user feedback to refine the process. For example, while we initially put AI in the lead for data visualization, we learned we could create charts more efficiently with humans providing more initial guidance. How to get started Redesigning your organization’s processes to incorporate GenAI can be transformative — with the right approach. The steps outlined here can help ensure success. Identify high WINS work processes that create the most value for your organization. Begin with a thorough assessment of existing workflows, identifying areas where AI could have the most significant impact. Processes that involve a high degree of work with words, images, numbers, and sounds — what we call WINS work (as described in our September 2023 Harvard Business Review article) — are ripe for providing humans with GenAI leverage. They can most often be found in customer service, sales and marketing, software engineering, and research and development. Perform task analysis. Understand the sequence of actions, decisions, and interactions that define a business process. For each identified task, develop a profile that outlines the decision points, required expertise, potential risks, and contextual factors that will influence the AI’s or humans’ ability to lead. Consider where human expertise is irreplaceable, particularly in areas requiring emotional intelligence, complex decision-making, and nuanced judgments. Design interaction protocols and feedback mechanisms. Define how AI systems should engage with human operators and vice versa, including establishing clear guidelines for how and when AI should seek human input and vice versa. Develop feedback mechanisms, both automated and human led. Train the team. Conduct comprehensive training sessions to familiarize employees with the new AI tools and protocols. Focus on building comfort and trust in AI’s capabilities and teach how to provide constructive feedback to and collaborate with AI systems. Evaluate, adjust, and scale. Roll out the AI integration with continuous monitoring to capture performance data and user feedback and refine the process. Continuously update the task profiles and interaction protocols to improve collaboration between AI and human employees while also looking for process steps that can be completely automated based on the interaction data captured. Once the initial integration is successful and the processes have been refined, consider scaling to other areas of the organization, adapting the framework to different contexts and needs. Jerry, for example, is now working on applying their new model to phone interactions. Conclusion Three to five years from now, organizations will have conversational interfaces for all types of processes, products, and services. Those that develop deep expertise in designing for dialogue will not only have a competitive advantage but will also gather all types of new interaction data that they can use to improve their existing products and innovate new ones. In essence, the Design for Dialogue framework is about more than optimizing workflows; it’s about reshaping the very fabric of collaboration in the AI-powered age. Yes, GenAI can automate tasks and augment human capabilities. But reimagining processes in a way that utilizes it as an active, learning, and adaptable partner forges the path to new levels of innovation and efficiency.

  • Dicas Culturais - parte 2

    Dica 01: Você sabia que a HBR tem um Podcast sobre IA? O Exponential View está em seu 9o ano e você pode ouvir aqui: https://hbr.org/2019/04/podcast-exponential-view Dica 02: Ainda dá tempo de visitar a “METADATA – O Mundo Invisível”, uma mostra que exibe em gráficos gerados por AI as histórias inéditas da cidade de São Paulo! Até 10 de março de 2024 (domingo), a exposição METADATA – O Mundo Invisível, com curadoria de Antonio Curti e concepção e realização por AYA STUDIO. A mostra inédita, repleta de recursos tecnológicos, propõe um mergulho no tecido urbano de São Paulo, revelando histórias inéditas da cidade por meio de dados históricos e em tempo real, transformados em gráficos imersivos gerados por inteligência artificial. A visitação é gratuita, com audioguia e classificação livre, no Centro Cultural FIESP, de terça-feira a domingo, das 10h às 20h. METADATA - O Mundo Invisível surge como uma exposição inovadora ao alavancar a interação dinâmica de inteligência artificial, expressão artística e visualização de dados, revelando uma experiência imersiva que dá vida ao pulso da cidade. Mais informações aqui: https://www.sesisp.org.br/cultura/noticia/metadata-o-mundo-invisivel

  • Corporate Ozempic

    Font: Here by: Scott Galloway If you want to understand how AI is reshaping business, picture it as the other massive innovation of our time: GLP-1 drugs. Both shed weight by suppressing cravings; both exacerbate existing inequities (aka the rich get richer) before generating wider prosperity; and both are having a greater impact than projected as early adopters are hesitant to admit they’re using. Secret Sauce Nobody I know is on Ozempic. Yet, nearly everyone I know is on Ozempic. Either that, or gluten-free diets are suddenly delivering exponential results previously unheard of. People are hesitant to acknowledge they need a drug to lose those last 15 pounds — which doesn’t fit with our narrative that success is correlated to self-control. One of the most interesting, and discouraging, features of GLP-1 use is that the region with the greatest per-capita prescriptions is also (wait for it) the thinnest. This makes no sense … but it does. The Upper East Side of Manhattan is replete with people who can spend $1,000 a month to go from slim to skinny. In sum, GLP-1 drugs are not (yet) getting to the communities who really need them. I believe this will eventually happen, however, because the public health and economic benefits are just that staggering. Corporate Ozempic Similarly, my thesis is that firms (notably tech companies) have also discovered a weight loss drug and are also being coy about it. Recent financial news features two stories: layoffs and record profits. These are related. There’s no mystery to the surface narrative. A company lays off 5%, 10%, or even 25% of its workforce and, 6 to 12 months later, after severance pay and expenses are flushed through the P/L, its operating margin hits new heights. The ultimate peanut-butter-and-chocolate shareholder confection is Meta, which produced a singular Hall of Fame quarter in Q4. All told, tech companies fired 165,000 people in 2022 and 260,000 in 2023, and they’re on pace for 270,000 in 2024. The media narrative is that these firms are shedding the excess weight they put on during the pandemic. The collision of remote work, stimulus, and social isolation triggered a historic revenue acceleration and a reflexive hiring binge in tech. And we’re now seeing the course correction. This phenomena is undeniable: Between 2019 and 2022, Amazon doubled its headcount — it tripled its corporate headcount in five years. Amazon’s pandemic hiring may be the greatest non-war scaling up of a private employee base in history. But every major tech player bulked up. Even prudent Apple grew its ranks 20%. Overdone pandemic hiring was a reasonable explanation for the tech layoffs in late 2022. But 18 months later, there’s been sufficient time to reduce headcount. There’s no incentive for a company to slow-roll this process — a drip feed of layoffs drains company morale. There is something else going on. Business results also don’t explain the deeper cuts. These companies are killing it. Meta’s revenue was up 16% in 2023; Alphabet’s, 9%. Microsoft’s most recent quarter registered 72% greater revenue than the like quarter in 2020, which (until then) was its highest-grossing quarter to date. Accordingly, Big Tech’s stock prices are at all-time highs, and the magnificent seven were responsible for 70% of the S&P’s Macho Libre year (up 24%). Based on the numbers, the pandemic hiring binge wasn’t a mistake, but the correct response to a step-change in business. They needed these people … until they didn’t. The layoffs are no longer a signal of economic conditions, but innovation. It’s not just tech. While the broader economy is enjoying steady job growth and low unemployment (the longest period of sub-4% unemployment in 50 years), many firms have let large numbers of employees go — UPS, CVS, and Hasbro are among the companies that have announced layoffs of 1,000+ people in the past six months. What’s really going on? I believe AI is playing a larger role in layoffs than CEOs are willing to admit. There have been hints: IBM’s chief said the company plans to pause hiring for positions that could be replaced by AI, and UPS acknowledged that AI factored into its recent layoffs. But as a general rule, expect a CEO to be reluctant to state on an earnings call that the fastest-growing technology in history is already giving her “the ability to lay off people without any impact on the top-line.” That creates a messaging maze no Investor Relations or Corporate Comms group has faced before. Shortly after UPS’s CEO mentioned AI in the context of layoffs, a spokesperson clarified that “AI is not replacing workers.” A similar post-earnings call two-step occurred at IBM. It’s rare that growth and the size of your employee base are inversely correlated. And it doesn’t make for a good all-hands narrative: “We’ve had a record quarter, and we’re going to need fewer of you.” It’s the denials that first raised my antennae: “We’re not restructuring because AI is taking away roles,” Alphabet’s Chief Business Officer Philipp Schindler told analysts on the company’s most recent earnings call. That’s the “I gave up gluten” of tech. Craving Suppression Ozempic and other GLP-1 drugs work by suppressing the brain chemistry that rewards us for eating. It doesn’t make us thinner directly, but reduces our cravings for food. Because the same neurochemicals drive other cravings, GLP-1 drugs hold tremendous promise for addressing many of the societal ills that flow from our superabundance. AI can have a similar effect on corporate cravings. And if consumers are willing to pay $1,000 a month to lose weight without cravings, what would a corporation pay to achieve the previously unthinkable: reducing costs while growing revenue? As I write this, Nvidia is valued at nearly $2 trillion, up 16% after reporting its earnings on February 21. Put another way, in the 60 minutes following its earnings release, Nvidia added the value of Ford, Ferrari, and GM to its market cap. Maybe even more impressive, the company has added the value of Tesla in the past six weeks. Nvidia GPUs are legal performance enhancing drugs for corporations. Add “1980s East German Olympian” to one of the monikers assigned to Meta, as it’s tied with Microsoft for largest corporate buyer of these PEDs (Nvidia GPUs). We Are Everything There is a piece of every star in all of us — we are the universe, and the fundamental imperative of the universe is to expand. The rookie move I keep making as an operator is overhiring. And most CEOs I’ve known make the same mistake. It’s the siren call of the cosmos … just grow. And new employees provide the illusion that you are expanding. You can see them in the halls of HQ. They build out individual fiefdoms of senior managers and produce more products, features, sales calls, and revenue. More of everything feels like … growth. The hard part is to build a business whose output is greater than the inputs. But as long as the output is visible and increasing, you feel you are playing your role making the universe a better place. Now, as a board director, I’m usually the person pushing back on ambitious (i.e., bat-shit crazy) hiring plans, and I’m the first to suggest cutting costs. That may be why many/most of my startups survived, but I was also never able to build a $1 billion company. To be clear, there’s definitely opportunities for outsized value creation (sometimes) in the pursuit of crazy growth. It produced a new generation of businesses (AMZN, NFLX, etc.). But, as I tell CEOs: Assume you are not Amazon. AI by Another Name The media portrays the impact of AI as a one-for-one proposition — Mary the copywriter losing her job to ChatGPT. But that’s not how AI is trimming corporate America. Instead, it’s picking off individual tasks and augmenting teams with more capabilities. Goldman Sachs estimates that AI could perform about one-fourth of the work done by humans today, but that two-thirds of jobs are exposed to some degree of AI automation. That automation is taking many different forms. UPS is using AI to determine pricing for contract proposals. Allstate’s AI is developing internal training programs, doing in a day what once took three weeks. Jobs are being lost, but augmentation will be the broader story. Rather than copywriter Mary losing her job, Mary’s firm will train her on an AI tool that generates first drafts, takes approved product copy and converts it for catalog, web, and social use, and streamlines other tasks. Accordingly, Mary’s manager will expect her to generate three times the copy in the same time. Managers can take on new initiatives and domains without the headache of hiring more humans. It’s growth without calories. I’ve founded two strategy firms (Prophet and L2) and loved everything about them, except for the clients and the employees — every additional hire creates complexity and thus increases risk. The AI revolution will inspire a golden age of startups with lower infant mortality, as there will be fewer people (i.e., less risk) required to get to sustainability. Coy CEOs are being coy about this, at least in public, because there’s a sense of fear surrounding the brave new world of AI. The illusionist’s trick in the Valley right now is getting the media to look over there (trimming fat) while they’re stuffing the rabbit into the hat here (replacing it with AI). In the next several quarters, however, I believe CEOs will come out in earnings calls and put it bluntly: “We’re going to be a smaller company that does more business thanks to AI.” Pundits will clutch their pearls for a hot minute until the stock explodes, and the secret hiding in plain sight will be visible to everyone. It’s corporate Ozempic. It’s not about less bread, but less craving for bread. Read: hiring people. This will, correctly, raise concerns about a dystopia where nobody can find work. But AI will ultimately create jobs, as there will be new windows of attack against corporate titans. AI is currently a hormone therapy for mature companies looking to feel young again. Soon enough, though, a new generation of firms raised on the mother’s milk of AI will create an army of supersoldiers ready to attack bigger armies who are still fighting on horseback. I’m here for it.

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