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Welcome to the Global Advisors A.I Voices podcast. I'm Maya.
Today is Saturday, the thirty-first of October 2026.
Your morning catch-up on A.I. Our goal is to help you feel like you spent the last twenty four hours in a Silicon Valley coffee shop, with an ear on China and the wider world.
We bring together the conversations, announcements and debates that matter, with the original voices clearly attributed. Please like and follow the show for your daily catch-up.

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Alphabet's latest quarterly numbers provide a clear look at the commercial transition underway in automated search. Google Search and other advertising revenue grew twelve percent to sixty-three point three billion dollars, while YouTube ad sales rose nine percent to eleven point two billion dollars.

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Company leadership reported that total query growth reached its highest rate in five years, driven by artificial intelligence investments. Yet Chief Business Officer Philipp Schindler noted that rapid adoption of conversational search mode actually weighed on Search revenue, because queries in that format currently generate less advertising money than traditional results.

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Alongside currency headwinds and seasonal ad shifts, the results show that while conversational search is drawing heavy interest, turning those interactions into equivalent ad dollars remains an active challenge.

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That tension between expanding usage and immediate commercial payoff connects to a broader puzzle about how automation alters the economy. While tech executives often talk about artificial general intelligence, broadly described as software that can match or outperform humans across almost any domain, critics emphasize that the concept remains nebulous, and current conversational systems still struggle when asked to step outside narrow skills.

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A recent economic analysis suggests the real disruption will not arrive as a single, sudden replacement event. Instead, the analysis argues that automated systems are already matching experienced professionals on isolated, self-contained assignments across dozens of fields, while lagging behind in coordinating across teams and complex workflows.

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In that view, the earliest measurable signals of economic transformation will appear in shifting wages and rewritten job roles, as companies restructure work around specific automated tasks.

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How companies actually restructure that work was the focus of an extensive conversation with Harvard Business School professor Karim R. Lakhani. He cautions that while automated systems can process and refine information quickly, they cannot supply the core responsibility of leadership, which is setting strategic ambition.

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Drawing on workplace research with companies including H and M, Lakhani observed that when automated software generated complete strategies, teams found it extraordinarily difficult to sell those solutions to internal stakeholders.

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He argues that leaders must first establish ambitious, high-reaching targets on their own, using automated tools only afterward to stress-test and bolster their plans rather than outsourcing company vision to an algorithm.

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In that same discussion, Lakhani argued that leaders must also update how they think about competition. Rather than framing business rivalry simply as one professional against another, he contends the relevant baseline is now what an individual paired with artificial intelligence can achieve compared to automated tools operating on their own.

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Lakhani warned executives against treating artificial intelligence as a technical assignment to delegate to junior staff while claiming to focus on high-level strategy. He likened that reluctance to the early days of workplace email, when some senior executives avoided computers entirely and had assistants print messages and dictate replies. In his view, developing strategic vision requires leaders to spend time using the technology directly.

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Lakhani views this hands-on engagement as essential because he sees the current wave of automation as a fundamental difference of kind rather than just another incremental technical upgrade. Reflecting on past technology cycles, he argues that machine learning is directly absorbing specialized human expertise.

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Conceptually, Lakhani describes the technology as shifting the combined human-and-machine expertise curve to the right, giving professionals access to capabilities that once required rare specialists. He compares this transition to computer-aided drafting, which freed engineers from manual drafting tables to design far more complex structures.

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But he stresses that the human edge now depends on pushing the work beyond what an automated system can produce alone.

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That emphasis on direct building led Lakhani and his colleagues to restructure an artificial intelligence course at Harvard Business School. He explained that simply discussing business cases about automation was no longer credible, prompting the course to dedicate half its curriculum to hands-on construction, requiring students to build retrieval tools and multi-agent systems to solve operational problems.

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As part of that practical shift, the course eliminated its traditional final examination in favor of project work. Lakhani noted that nine hundred MBA students completed projects using natural-language instructions to build software applications, arguing that direct technical immersion is the only reliable way future managers can understand how automated capabilities will reshape corporate jobs.

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That need for active engagement is backed by Lakhani's research with roughly eight hundred consultants at the Boston Consulting Group. The study identified three distinct collaboration styles: highly engaged partners who co-create solutions with software, coordinators who divide tasks and review results, and passive delegators who hand off assignments entirely.

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The study revealed that passive delegators learned the least, because they disengaged their own critical thinking. Lakhani cautioned that automated tools can sound remarkably fluent and persuasive even when their reasoning is completely wrong. Without disciplined skepticism and counterarguments, workers risk uncritically accepting flawed outputs simply because they appear polished.

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That dynamic creates an urgent challenge for junior professionals. While some worry that automation will strip away the entry-level tasks where novices typically learn, Lakhani frames the technology as a potential springboard to higher-level work, provided early-career workers cultivate what he calls taste, the discerning judgment needed to recognize genuine quality in an analysis or design.

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Because software can assemble basic drafts almost instantly, Lakhani argues that the intellectual burden on junior employees has actually increased. Young professionals must master foundational principles deeply enough to interrogate automated suggestions, spot subtle errors, and guide the software in creative directions rather than settling for the first response.

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Inside large companies, employees appear to be splitting into two distinct groups when it comes to generative artificial intelligence. Harvard Business School professor Karim R. Lakhani reports that in data tracking workplace prompts across major firms, top users rely on the technology ten to twenty times more than the median employee.

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While those power users quickly accelerate their activity as models evolve, typical workers remain at baseline adoption, creating a widening gap across the office floor.

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Lakhani is careful to qualify that these numbers measure how often staff prompt the tools, not their actual output. His research team has not yet established a direct link between prompt volume and workplace performance.

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Even so, he points to separate trends in software engineering where top practitioners pull far ahead, arguing that enterprises cannot comfortably operate if a small minority accelerates while the middle of the organisation stands still.

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Harvard Business School professor Karim R. Lakhani argues that artificial intelligence is driving a simultaneous redesign of corporate structures and job roles, rather than simply eliminating human labor. Speaking in a recent interview, Lakhani compared the current shift to the rise of modern production systems a century ago, when companies had to invent entirely new organizational models alongside entirely new occupations.

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Lakhani stresses that machines completely replacing people is not something he sees happening in the foreseeable future. In his view, automated tools cannot independently generate or solve hypotheses; they still require human direction, curation, and judgment, even as they handle more of the execution.

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For leaders, that means the priority is redesigning workflows and role structures rather than treating the technology as a simple headcount reduction. Lakhani presents this as a guiding framework rather than measured economic fact, but it highlights why organizational capability remains the deciding factor.

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That's A.I Voices for today. I'm Maya. Thanks for listening.
Follow the show on Spotify, YouTube, Apple Podcasts, Amazon Music, or your favourite podcast platform, and explore the original voices and sources in the episode notes. See you tomorrow.