“Today’s AI differs from previous generations’ because it can tell stories and create images. Built from online human stories rather than facts or logic, generative AI mimics human intelligence by collecting and recombining our digital narratives. While earlier AI managed specific organizational functions, generative AI directly addresses how humans think and communicate. Unintended consequences: Because generative AI is built from people’s digital commentary, it inherently propagates biases and misinformation.
More fundamentally, it doesn’t actually ‘think’ — it simply plays back combinations of stories it has seen, sometimes producing recommendations with completely unintended effects or removing human agency entirely. Since humans choose actions based on stories they believe, and collective action depends on consensus stories, generative AI’s ability to tell stories gives it worrying power to directly influence what people believe and how they act — a power earlier AI technologies never possessed” (MITSloan, 12 Nov 2025).
“The world is spending more on data centers than on new oil supplies, signaling a major shift in the global economy. According to a new report from the International Energy Agency (IEA), spending on data centers this year will reach $580 billion, which is $40 billion more than what will be spent on new oil production, as reported by TechCrunch” (Economic Times, 11 Nov 2025).
How the productivity reset will redefine value. “In the coming years, productivity may no longer be captured solely by the traditional ratio of quantity of output to units of input. What has so far been a relentless race for increased quantity may flip on its head, as we enter an era in which machines can generate seemingly infinite quantities of content. The focus will instead shift to quality and creativity, driven by the increasingly critical capacity of organizations to convert information, insight and innovation into sustained economic value. The challenge will be how to quantify and incentivize gains that occur not on production lines, but within digital ecosystems, in the speed of decision-making, the adaptability of autonomous systems, and utilizing the creativity unlocked through human-machine collaboration” (EY Megatrends, 12 Nov 2025).
How superfluid enterprises reshape organizations for competitive edge. “Technological advancements are allowing companies to eliminate operational frictions in ways that were previously impossible. This includes rapid progress in AI, the maturation of blockchain and quantum computing — all accelerated by an environment where technology is evolving quickly and can suddenly reach tipping points that catch companies off guard (such as AI’s ‘ChatGPT moment’). Additionally, resource scarcity and an increasingly urgent climate crisis require more efficient supply chains, while geopolitical volatility raises the need for operational structures that can respond swiftly to changing events, all of which make superfluid enterprises more appealing. The question facing leaders today isn’t whether this transformation will happen, but whether their organization will lead it or be swept along by competitors who embrace superfluidity first” (EY Megatrends, 12 Nov 2025).
Are US States Ready for the AI Economy? “Economic and workforce-development leaders throughout the US are in broad agreement on the importance of AI: 88% of them see the technology as crucial to the competitiveness of their economies. But fewer than 10% say their state has a well-defined strategy in place for responding to AI’s economic impact…. Our research offers public sector stakeholders a four-part playbook for transformative action on AI” (Boston Consulting Group, 12 Nov 2025).
Even AI can’t save the US economy from one of its biggest risks. “[Mark] Zandi raised concerns that the benefits of AI are largely confined to the already wealthy, which could limit its contributions to overall economic growth, even if the AI boom continues to drive up stock prices. ‘Our already highly skewed income and wealth distribution will become even more so,’ he predicted. ‘The economic and political struggle between the haves and have-nots will intensify, to everyone’s detriment'” (Business Insider Africa, 12 Nov 2025).
AI is physical: Investing in the infrastructure behind intelligence. “For years, conversations about artificial intelligence centered on algorithms, models, and code. But the real story of AI’s next decade isn’t digital, it’s physical. It might seem that AI lives in the cloud, focusing attention on the platforms that connect it to people – such as ChatGPT, Anthropic, and Perplexity – and that that is where the investment should be. But the vital infrastructure for AI is physical: the servers, substations, and semiconductors that make the cloud possible” (Wealth Professional, 11 Nov 2025).
The State of AI Innovation and Deployment. “I see two basic scenarios for how AI can transform the economy. In the first scenario, there is incremental adoption of GenAI that augments existing tasks and jobs. In the second scenario, a revolution occurs. GenAI transforms the nature of work and leisure, boosting the efficiency of research and development, remaking industries, and creating firms with new—perhaps radically new—business models. Right now, it is difficult to predict which scenario (or perhaps one or more intermediate scenarios) will come to pass” (Federal Reserve).
The AI multiplier effect. “Proprietary data—the structured and unstructured data that an organization intentionally collects and stores for its operational and decision-making processes—can provide a significant strategic advantage. 72% of CEOs go so far as to say that proprietary data is key to unlocking the value of generative AI. Yet, many organizations struggle to use their data to power AI. CDOs [Chief Data Officers] agree that the top data barriers they face on this front are accessibility, completeness, integrity, accuracy, and consistency. Fortunately, AI agents can help address these challenges—if organizations unleash AI on an optimized data estate” (IBM, 12 Nov 2025).
Millennials’ Total Net Worth Has Nearly Quadrupled Since Covid. “Millennials might have entered the pandemic worried about their finances, but five years later, their wallets are thriving more than ever. According to Federal Reserve data, millennials’ total net worth has nearly quadrupled since 2019, growing from $3.94 trillion in Q3 2019 to $15.95 trillion in Q3 2024. They’ve not only weathered the post-pandemic years but have also accumulated wealth faster than older generations” (Yahoo! Finance, 12 Nov 2025).
“ML [Machine Learning] is redefining how teams plan, monitor, and deliver. Predictive modeling is at the center of that shift. By learning from past projects, these ML models can forecast what’s likely to happen next, helping teams act before challenges surface. Some common types of predictive models include classification, regression, time series forecasting, survival analysis, and anomaly detection models. For example, Random Forest and Gradient Boosting are widely used classification and regression models that can predict project risks or cost overruns. Time series models such as ARIMA or LSTM are used to forecast project schedules and resource needs. Whether predicting cost or schedule pressures, managing resources or spotting early signs of risk, the goal is the same: to build projects that are completed on time and on budget” (PwC, 12 Nov 2025).
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