Harshil Mathur, CEO of Razorpay, argues that every founder and CEO should personally build at least one AI workflow they use on a daily basis. He contends that without this hands-on experience, leaders will inevitably either overestimate or underestimate AI's capabilities.

The advice underscores the importance of direct, practical engagement with AI tools rather than relying on secondhand assessments or abstract strategic discussions. By embedding AI into their own daily routines, founders can develop a grounded, realistic understanding of what AI can and cannot do — which is critical for making informed decisions about AI adoption across their organizations.

Barry O'Reilly shares his personal journey from burnout and overwhelm to building an AI-powered productivity system that transformed how he works as a leader. Drawing from nearly a decade of experimentation—starting with tools like Rev.com for dictating his book and Otter.ai for meeting notes—he describes how he systematically identified tasks to offload and automate, ultimately reclaiming hours each week for strategic thinking and deep work.

The article presents findings from a survey of over 5,000 leaders, revealing that 72% don't know where to start with AI, 61% lack a clear AI strategy, yet 92% believe AI could significantly boost their productivity. O'Reilly argues that the core challenge is strategic and behavioral, not technical. He outlines four key principles: start with tasks rather than tools, treat AI like a junior analyst rather than a magic wand, design workflows before automating them, and pair AI's velocity with strategic clarity. Real-world examples show leaders saving 5–10 hours per week through targeted AI integration.

The piece pushes back against the tendency to simply play with AI tools for the dopamine hit, urging leaders to shift from prompt-playing to workflow design and from task automation to outcome architecture. O'Reilly offers free assessment tools and cohort programs as next steps, concluding with the insight that AI amplifies existing leadership habits—making intentional calendar and priority design essential before layering on technology.

Jesse Middleton, who watched WeWork grow from 4 people to 4,000, reflects on the critical difference between adding headcount and increasing output. He highlights a fundamental organizational challenge: a team of 4 has only 6 lines of communication, while a team of 12 has 66. Every new hire brings additional capacity but also introduces more context-sharing overhead and coordination complexity.

The post underscores a well-known but often underestimated principle in scaling organizations — that communication complexity grows exponentially while capacity grows linearly. Middleton's firsthand experience at WeWork lends credibility to this observation, serving as a cautionary insight for founders and leaders navigating rapid team growth.

Aaron Levie, CEO of Box, shares observations from a week of meetings with IT and AI leaders across major enterprise sectors including banking, media, retail, healthcare, consulting, tech, and sports. The focus of these conversations was on agents in the enterprise — the next phase of AI adoption.

Levie signals a key industry shift: enterprises are moving from the chat era of AI to an agentic model, though the full thread of takeaways is truncated in the archived content. The post reflects growing momentum around AI agents as the central topic among large enterprise decision-makers, suggesting this transition is becoming a strategic priority across industries.

Microsoft's 2026 Work Trend Index report, drawing on trillions of anonymized Microsoft 365 productivity signals and a survey of 20,000 AI-using workers across 10 countries, argues that AI and agents are fundamentally expanding human agency at work — but most organizations aren't built to capture the value. The report introduces the concept of a "Transformation Paradox": employees are ready to reinvent how they work with AI, but organizational metrics, incentives, and norms continue to reinforce legacy ways of working. Only 19% of AI users occupy the "Frontier" zone where both individual capability and organizational readiness are high, while the largest share sits in an "emergent" middle ground of misalignment.

The research identifies Frontier Professionals — 16% of AI users who deploy agents for multi-step workflows, routinely redesign processes, and build shared AI standards — as disproportionately valuable. These users prioritize critical thinking and human judgment, intentionally doing some work without AI to keep skills sharp. Crucially, the report finds that organizational factors like culture, manager support, and talent practices account for more than 2x the reported AI impact of individual effort alone, underscoring that the biggest lever for AI value isn't hiring better people but creating the right environment.

The report calls on organizations to become "Learning Systems" — firms that capture signals from agent-driven work and encode them into shared routines, building what Microsoft calls "Owned Intelligence" that compounds over time. It outlines coordinated roles for employees, leaders, IT, and security in building an evaluation infrastructure for agent operations at scale. Manager behavior proves especially impactful: when managers actively model AI use, employees report a 17-point lift in AI value, a 22-point lift in critical thinking, and a 30-point lift in trust in agentic AI.

Rawad Baroud, CEO of ZeroGPT, argues that AI's greatest value for leaders isn't speed or output — it's serving as a thinking partner that helps reduce mental friction. He observes that leadership often feels overwhelming not because tasks are impossible, but because too many unstructured thoughts compete for attention. Rather than treating AI as a "second brain" that thinks for you, Baroud suggests using it to clarify messy thinking — translating vague concerns into decision memos, mapping stakeholder perspectives before difficult conversations, running project premortems, and pressure-testing initiatives.

The article cautions against over-reliance, emphasizing that a polished AI response is not necessarily a correct or complete one. Leaders must still bring context, values, experience, and accountability to every decision. Baroud also highlights practical risks: sharing sensitive company information without safeguards, AI reflecting bias, and the tendency to flatten complex human situations into something that sounds logical but lacks empathy. He advocates for clear boundaries — using AI to clarify, question, and structure, but never outsourcing responsibility or human judgment. The core takeaway is that AI, used thoughtfully, can function less like a shortcut and more like a stabilizer for leadership thinking.

Benjamin Todd argues that AI is most likely to automate entry-level white collar jobs first, leading to increasingly top-heavy organizations where traditional career training pipelines shrink. This poses a significant challenge for early-career professionals who may find fewer opportunities to develop skills through conventional junior roles.

He offers practical advice for those early in their careers, suggesting they should seek out mentorship on leadership skills as soon as possible, since these higher-level competencies are likely to remain valuable and harder to automate. The post implies a broader shift in how career development needs to be approached in an AI-driven economy.

David De Cremer and Garry Kasparov argue in this Harvard Business Review article that AI should augment human intelligence rather than replace it. They highlight that AI is projected to add as much as $15.7 trillion to the global economy by 2030, but caution against the growing trend of organizations displacing human workers with intelligent machines across manufacturing, service delivery, recruitment, and finance.

The authors emphasize that people and AI bring different abilities and strengths to the table. Rather than viewing AI as a wholesale replacement for human workers — a trend that could make the workforce "totally unrecognizable" by 2040 — the real question organizations should be asking is how human intelligence can effectively collaborate with artificial intelligence to produce better outcomes than either could achieve alone.

Egon Zehnder explores how AI is transforming the product management function across three critical dimensions: shaping the product (strategy, discovery, goal setting), shipping the product (execution, QA, resource management), and communication and collaboration. AI-powered tools now enable data-driven product strategy at unprecedented scale, assist in drafting user stories via NLP and generative AI, predict project risks, and streamline cross-functional coordination through automated meeting summaries and stakeholder updates.

The article argues that AI is not replacing product managers but elevating the role toward higher-value activities. As tactical tasks become automated, PMs must shift focus to strategic vision, user-centric innovation, ethical considerations, and emotional intelligence. The piece categorizes PM tasks into three tiers: those significantly impacted by AI (strategy development, QA, roadmap optimization), those enhanced but still human-driven (spec creation, stakeholder communication), and those that will always require a human touch (critical thinking, creative problem solving, strategic storytelling).

Key recommendations include upskilling in data literacy and strategic thinking, leveraging AI tools while maintaining human oversight, and fostering organizational cultures of continuous learning and experimentation. The core takeaway is that product managers who learn to pair machine intelligence with human insight will define the future of the function, while those who ignore AI risk being left behind.

Eric Jing shares the third installment in his "Seeing AGI" series, focusing on the concept of building AI-native teams. He argues that creating such teams goes beyond simply adopting new tools — it requires a fundamental rethinking of how organizations structure, scale, and execute their work. Drawing on nearly 20 years of experience across Search, Hardware, and AI, Jing positions this as a critical shift in organizational design rather than a mere technology upgrade.

The post appears to be a longer-form piece linked from X (formerly Twitter), suggesting a deeper exploration of what it means to operate as an AI-native organization. This is part of an ongoing series where Jing examines the path toward AGI from practical, operational perspectives.