32 articles in Artificial Intelligence
McKinsey's comprehensive survey of AI adoption across industries. Covers generative AI's explosive growth, ROI data from early adopters, workforce implications, and strategic recommendations. Shows that high-performing AI organizations invest in talent, data infrastructure, and responsible AI practices. Used widely in executive education programs.
An accessible introduction to how LLMs like GPT-4 and Claude work. Covers tokenization, embeddings, the transformer architecture, attention mechanisms, and training (pre-training, fine-tuning, RLHF). Explains emergent capabilities, hallucinations, context windows, and temperature. Designed for product managers, designers, and business leaders who need to understand AI without deep technical background.
A comprehensive introduction to prompt engineering for professionals. Covers key techniques: zero-shot and few-shot prompting, chain-of-thought reasoning, role prompting, and structured output. Practical examples for content creation, data analysis, and decision support. Increasingly part of digital literacy curricula at universities.
Explores how AI is transforming the workplace. Argues that AI won't replace humans, but humans with AI will replace humans without AI. Covers how to prepare teams for AI adoption, which tasks are most susceptible to automation, and how to develop AI-complementary skills. Essential reading for leaders navigating the AI transition.
McKinsey's landmark analysis estimating generative AI could add $2.6-4.4 trillion annually to the global economy. Identifies four industry sectors most impacted: customer operations, marketing/sales, software engineering, and R&D. Details 63 generative AI use cases across 16 business functions. Shows that about 75% of the value falls in just four areas. The definitive business case for AI investment.
While automation and AI will transform 60% of current jobs, the most durable human skills, including empathy, creativity, ethical judgment, and complex communication, are gaining rather than losing value. McKinsey's analysis shows that demand for social-emotional skills will grow 24% by 2030, while demand for routine cognitive skills declines. The article maps which capabilities to invest in for long-term career resilience and how organizations should redesign roles to combine human strengths with AI capabilities.
Organizations lose an estimated 40% of institutional knowledge each year through attrition, yet most knowledge management systems capture only explicit, documented knowledge while ignoring the tacit expertise that drives actual performance. AI tools now offer new ways to capture, organize, and distribute tacit knowledge through conversation analysis, expert network mapping, and automated documentation. The article presents a maturity model for knowledge management that integrates AI capabilities with human expertise networks.
A practical guide for product managers working with ML teams. Covers the ML product lifecycle, how to frame problems as ML problems, data requirements, evaluation metrics, and common pitfalls (data leakage, overfitting, bias). Teaches PMs enough to be dangerous without requiring deep technical knowledge.
The Center for Humane Technology and similar organizations have catalyzed a movement toward digital wellbeing — designing technology that supports rather than undermines human flourishing. This article explores the principles of humane design: respecting users' time, minimizing compulsive usage, supporting intentional engagement, and giving users genuine control. It covers practical design patterns (usage dashboards, focus modes, thoughtful notification design) and organizational practices (wellbeing impact assessments, design ethics reviews) that product teams can implement to create technology people are grateful for rather than addicted to.
Netflix attributes over 80% of content watched to its recommendation system. This case study traces the evolution from the Netflix Prize competition to modern deep learning approaches, examining how product and engineering teams collaborate to personalize content for 230 million subscribers across diverse global markets.
TikTok's recommendation algorithm is widely considered the most sophisticated content discovery system ever built for consumer social media. This case study examines how the For You Page works, how the product team balances engagement metrics with user wellbeing, and what the algorithmic feed model means for the future of content platforms.
Instagram's 2016 shift from chronological to algorithmic feed was one of the most controversial product decisions in social media history. This case study examines the data behind the decision, how the team iterated on ranking signals, managed user backlash, and ultimately increased engagement while setting a template that every social platform would follow.
Spotify's Discover Weekly and Wrapped features are masterclasses in using data to create delightful product experiences. This case study examines how the data science and product teams collaborate, how Discover Weekly's recommendation engine was built by a small team in a hackathon, and how Wrapped turned personal data into a viral annual marketing event.
A balanced assessment of Web3 technologies — blockchain, smart contracts, and decentralized applications — examining genuine use cases alongside the hype and limitations.
A non-technical introduction to quantum computing for business leaders, covering what quantum computers can and cannot do, timeline expectations, and how to prepare.
Shoshana Zuboff's concept of surveillance capitalism describes an economic system where human experience is claimed as free raw material for hidden commercial practices. This article summarizes the key arguments: how behavioral surplus is extracted, how prediction products are manufactured and sold, and how instrumentarian power shapes behavior at scale. It then examines the business alternatives — privacy-preserving business models, federated learning, differential privacy, and data cooperatives — arguing that the current data economy model is neither inevitable nor sustainable.
As algorithms increasingly make decisions about hiring, lending, criminal justice, and healthcare, the question of fairness becomes urgent. This article introduces the key concepts of algorithmic fairness: different mathematical definitions of fairness (demographic parity, equalized odds, individual fairness), why they are often mutually incompatible, and the sources of bias in training data and model design. It provides a practical framework for fairness audits, bias mitigation techniques, and the organizational processes needed to embed fairness considerations into the ML development lifecycle.
In an information-rich world, attention is the scarce resource. This article examines how the attention economy works: the business models built on capturing and monetizing human attention, the design patterns that exploit cognitive vulnerabilities (infinite scroll, variable reward schedules, social validation loops), and the societal consequences including shortened attention spans, political polarization, and mental health impacts. It also explores alternatives — attention-respecting business models, humane technology design, and the growing movement for digital minimalism.
An introduction to edge computing — moving computation closer to data sources — and its implications for application architecture, user experience, and business models.
From GDPR in Europe to China's algorithmic regulation to US antitrust actions, the global regulatory landscape for technology is evolving rapidly. This article provides a comprehensive overview of the major regulatory frameworks affecting technology companies: data protection and privacy laws, content moderation requirements, competition and antitrust enforcement, AI-specific regulation, and digital taxation. For each area, it explains the policy rationale, key provisions, and practical implications for product and engineering teams. It argues that proactive compliance is not just legal necessity but competitive advantage.