The Prof G Pod with Scott Galloway

Why CEOs Are Getting AI Wrong — with Ethan Mollick

February 12, 2026

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  • The immediate, tangible impact of AI is seen in individual productivity gains (e.g., 40% improvement in quality, 26% faster work in early studies), but organizational redesign necessary to realize systemic benefits is lacking imagination. 
  • The primary choke points currently hindering the widespread adoption and scaling of AI are energy/power grid capacity and the physical construction of data centers, rather than data availability or research breakthroughs. 
  • The apprenticeship model for entry-level professional learning is breaking down as AI tools are used by interns and junior staff to bypass foundational skill acquisition, necessitating a greater formal role for education. 
  • The strategy of releasing AI models as open weights, employed by Chinese companies (also RANs) and Mistral, threatens to shift value away from proprietary models like those from OpenAI or Anthropic once the open models catch up in capability. 
  • Ethan Mollick, as a father, advises his children to pursue diverse careers where they perform many different tasks, acknowledging the uncertainty of future job roles due to AI, while emphasizing the importance of resilience and improvisation over specific career planning. 
  • Societal and governmental policy decisions, rather than individual preparation, are crucial for managing risks associated with AI, including deepfakes, job displacement, and the long-term effects of parasocial relationships with synthetic AI characters. 

Segments

Activism Metrics and Media Halo
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(00:01:09)
  • Key Takeaway: The success of boycotts and movements relies more on media coverage of potential economic impact than on immediate, measurable economic decline.
  • Summary: The ‘resist and unsubscribe’ campaign achieved significant initial traffic (up to 100,000 uniques daily) without paid marketing, driven by the media’s coverage of its potential impact. Successful movements often leverage media attention to create momentum among employees and partners, even if direct economic impact is lagging. Traditional media retains substantial relevance because snippets from its coverage drive broad online distribution and clicks.
Quantifying Activism Impact
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(00:04:48)
  • Key Takeaway: Intentional, self-driven traffic to an action site can yield high conversion rates (estimated 3%) compared to typical e-commerce benchmarks.
  • Summary: The campaign generated over 16 million views across social platforms, translating to nearly 600,000 site visits since February, demonstrating economic coordination rather than passive outrage. Based on 100,000 daily visitors and a 3% unsubscribing rate across three platforms, the effort could equate to a $300 million notional market cap hit monthly. The core message is that individuals with a small footprint can take coordinated economic action with minimal personal effort.
Overcoming Fear of Failure
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(00:09:07)
  • Key Takeaway: The primary obstacle to taking impactful action is the fear of public failure, which is psychologically insignificant in the long term.
  • Summary: The key to having a voice and making a difference is overcoming the fear of public failure, such as launching an initiative that receives no response. People who punch above their weight class recognize that the risk of public failure is a small barrier compared to the potential reward. Living a self-actualized life requires acting as if no one is watching, recognizing that in a century, no one will remember minor public setbacks.
AI Existential Risk vs. Current Disruption
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(00:12:38)
  • Key Takeaway: While AI lab CEOs express existential anxiety, the more immediate concern should be guiding the next few years to ensure AI empowers workers and students rather than causing negative consequences.
  • Summary: Anthropic CEO Dario Amode’s pessimistic essay reflects a sincere anxiety about humanity’s maturity to wield unimaginable power from AI. Ethan Mollick focuses less on distant existential risks and more on the disruptive realities of today, such as modeling work correctly to empower employees rather than cause job loss. The focus must be on nitty-gritty guidance for AI in education, work, and society to mitigate immediate negative risks like deep fakes and dependency.
Enterprise AI Adoption and Productivity
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(00:15:40)
  • Key Takeaway: Enterprise AI adoption is currently underpenetrated, with many workers using AI privately for workload reduction rather than officially for corporate gains, fearing job displacement.
  • Summary: Studies show significant individual productivity gains (e.g., 40% quality improvement at BCG using GPT-4), but companies are not seeing this reflected centrally because workers hide their usage. Workers report three times the productivity gains on tasks where they use AI but do not report this to employers, fearing efficiency will lead to layoffs. Successful organizational deployment requires a combination of leadership setting direction and empowering the ‘crowd’ to experiment and harvest use cases.
Defining Agentic AI and Tech Stack
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(00:19:16)
  • Key Takeaway: Agentic AI refers to an AI tool granted access to external tools (like web search or code execution) to autonomously pursue a given goal.
  • Summary: The ‘jagged frontier’ describes AI’s uneven capabilities, but agentic AI represents the next step by allowing the model to execute multi-step tasks autonomously. A recommended starting tech stack involves subscribing to one of the ‘big three’ advanced models (GPT-4o, Claude 3 Opus, or Gemini 1.5 Pro) for about $20 monthly. Beginners should start by asking the AI to assist with all daily job tasks to map the AI’s current strengths and weaknesses.
AI Model Competition Dynamics
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(00:25:28)
  • Key Takeaway: The leading AI models from Google, OpenAI, and Anthropic are locked in a tight, week-by-week race driven by the scaling laws, where larger models generally outperform smaller ones.
  • Summary: The underlying dynamic is the scaling laws, meaning larger models trained on more data and compute are inherently better, limiting the field to a few well-funded players. The top three models constantly copy new features from each other, leading to rapid convergence in capability, though they exhibit distinct ‘personalities’ (e.g., Anthropic being more cautious, ChatGPT being more direct). The long-term outcome is uncertain, potentially leading to one model achieving self-improvement (’takeoff’) or the technology becoming commoditized.
AI Impact on Higher Education
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(00:45:28)
  • Key Takeaway: AI is disrupting teaching methods (like essay assignments) but is unlikely to make higher education obsolete; instead, it may increase the value of formal education as informal apprenticeship learning declines.
  • Summary: AI is causing disruption by making traditional assessment methods like essays obsolete due to cheating, forcing educators to pivot toward experiential learning. The value of professional education may rise because AI is already replacing entry-level intern work, breaking the traditional apprenticeship loop where junior staff learn by performing grunt work. This necessitates formal education to teach skills that informal, on-the-job learning previously provided.
AI’s Role in Academic Research
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(00:50:47)
  • Key Takeaway: AI is accelerating research output significantly, but this flood of AI-assisted content is simultaneously straining the traditional peer-review system by scrambling quality signals.
  • Summary: AI acts as a powerful research assistant, capable of finding complex errors in academic papers that require running new analyses. The volume of AI-produced research makes it difficult for human reviewers to filter worthwhile papers, leading to a crisis in academic publishing. Including AI in the peer-review process may become necessary to manage the influx of AI-generated scholarly work.
AI Potential in Medicine and Industry
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(00:51:53)
  • Key Takeaway: Medicine is an exciting area for AI due to its potential to accelerate research and streamline administrative tasks, though adoption is slowed by regulation and complexity.
  • Summary: AI can autonomously conduct directed research, potentially leading to a flood of new discoveries, and streamlines administrative burdens in drug development like Moderna utilizes. LLMs are effective for second opinions on diagnostic text but should not yet be trusted for image-based specialties like radiology. Realizing these gains requires significant leadership and structural change within complex, regulated organizations.
Open Weight AI Models Threat
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(00:56:11)
  • Key Takeaway: Open-weight AI models from competitors like Chinese firms and Mistral can destroy the market value of closed models if they achieve comparable capability, as users only pay for operational costs like power and security.
  • Summary: Releasing AI models with open weights allows anyone to run them internally, meaning the only cost incurred is for power, electricity, security, and network access, not licensing fees. Currently, these open models are less capable than leading proprietary models, but if they catch up, significant value will flow out of the closed-model ecosystem. This strategy is being adopted by companies like the Chinese also RANs and the European company Mistral.
AI’s Impact on Parenting
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(00:57:41)
  • Key Takeaway: Parenting in the age of AI requires focusing on preparing children for careers that demand diverse tasks and adaptability, rather than specific, singular paths, while cautiously integrating AI tools for educational support.
  • Summary: The uncertainty introduced by AI has shifted the speaker’s view on careers, favoring jobs that involve diverse tasks in case some are automated. The speaker uses AI cautiously with his children, insisting on explanations tailored for a ninth-grader or using AI in a ‘quizzing mode’ to challenge them rather than simply provide answers. The core parenting goal is fostering resilient, self-reliant children capable of improvisation.
Catastrophizing vs. Policy Needs
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(01:00:57)
  • Key Takeaway: While individual catastrophizing about AI apocalypse scenarios is unhelpful, government focus on creating policies for catastrophic risks, deepfakes, and societal integration is necessary.
  • Summary: The speaker acknowledges the value of smart people worrying about catastrophic AI risks, arguing that governments must develop policies and procedures rather than individuals preparing for the apocalypse. Immediate societal risks requiring policy responses include deepfakes, ensuring AI leads to better outcomes rather than immediate job loss, and managing parasocial relationships with AI systems. Age-gating synthetic relationships is suggested as a cautious measure due to unknown long-term effects.
Career Advice for Young People
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(01:04:01)
  • Key Takeaway: Careers are long and evolutionary processes, requiring young people to prioritize flexibility, experimentation, and agency over adhering to a rigid, pre-defined plan based on current skill sets.
  • Summary: The speaker’s own career path involved consulting, launching a startup with an embedded paywall, getting an MBA, and pursuing a PhD, demonstrating significant evolution. Young professionals should resist the tendency to believe they need to know everything required for the next step perfectly before taking it. Finding a path forward relies on experimentation and using personal agency rather than following a predefined track, a principle unlikely to change due to AI.