Why AI will dwarf every tech revolution before it: robots, manufacturing, AR glasses from CES 2026
Timestamps are as accurate as they can be but may be slightly off. We encourage you to listen to the full context.
Podcast Summary
This episode features Jason Calacanis in conversation with Bob Sternfels, CEO of McKinsey & Company, and Hemant Taneja, CEO of General Catalyst, at CES 2026. The discussion explores the transformative impact of AI on business and society, comparing it to the biggest technological revolutions of the past 30 years. (00:42) The conversation covers the unprecedented pace of innovation since ChatGPT's launch, the changing nature of work and workforce transformation, and the evolving strategies of both consulting firms and venture capital. Key themes include the compression of value creation timelines, the simultaneous expansion and contraction of different workforce segments, and the emergence of new business models where VCs are acquiring traditional businesses to accelerate AI transformation.
- Main themes: AI's revolutionary impact on business models, workforce transformation, the acceleration of innovation cycles, and new approaches to venture capital and consulting in an AI-driven economy.
Speakers
Bob Sternfels
Bob Sternfels is the CEO of McKinsey & Company, one of the world's largest and most influential consulting firms. He leads the organization through its own AI transformation, implementing strategies that have resulted in unprecedented changes to their business model. Under his leadership, McKinsey is simultaneously growing client-facing staff by 25% while reducing non-client-facing staff by 25%, demonstrating the practical application of AI in workforce transformation.
Hemant Taneja
Hemant Taneja is the CEO of General Catalyst, a venture capital firm with $40 billion in assets under management. He has pioneered a new approach to venture capital that goes beyond traditional startup investment to acquiring and transforming entire industries, including purchasing a health system in Ohio. Taneja led investments in companies like Stripe (2010) and Anthropic, demonstrating his ability to identify transformative technologies early in their development.
Key Takeaways
Embrace Simultaneous Growth and Contraction
Organizations need to prepare for unprecedented workforce transformation where they'll be hiring aggressively in some areas while cutting significantly in others. (09:18) Bob Sternfels revealed that McKinsey is growing client-facing staff by 25% while reducing non-client-facing staff by 25% with 10% increased output. This represents a fundamental shift from traditional business models where growth always meant total headcount increases. Companies must identify which roles move "up the stack" to more complex problem-solving while automating routine functions. This approach allows organizations to capture AI's efficiency gains while still investing in human capabilities that create competitive advantage.
Focus on Skills AI Cannot Replace
The future workforce will be defined by three core human capabilities: aspiration, judgment, and creativity. (23:19) As AI handles routine tasks, professionals must develop skills in setting ambitious goals, making value-based decisions, and thinking orthogonally beyond inference models. This means less emphasis on where you went to school and more focus on demonstrating raw capabilities like leadership, ethical decision-making, and innovative thinking. The implication is profound: talent evaluation should shift from credentials to actual capability demonstration, potentially widening access to opportunities for those who can prove these uniquely human skills.
Adopt Radical Collaboration and Iteration
Success in an AI-driven world requires constant iteration and trust-building with stakeholders rather than precise, narrow execution. (21:21) Hemant Taneja emphasized that founders must engage customers in co-creation, saying "we're gonna go figure this out together" rather than delivering finished solutions. This represents a shift from the traditional startup model of finding a narrow edge and scaling it to one of continuous adaptation based on rapidly evolving AI capabilities. Companies that build strong relationships and maintain flexibility to pivot will outperform those that rely on static business models.
Prepare for Compressed Value Creation Timelines
The timeline from startup to massive scale has compressed dramatically, requiring new frameworks for business planning and investment. (06:03) Anthropic grew from $880 million to potentially $8-10 billion in revenue in one year, representing 10x growth that previously took companies like Stripe 12-13 years to achieve. This compression means businesses must be prepared for exponential rather than linear growth, requiring scalable infrastructure, adaptable organizations, and different approaches to talent acquisition and capital allocation. The implication is that traditional 3-5 year business plans may be obsolete.
Create Your Own Path Into the Workforce
Young professionals cannot rely on traditional hiring processes and must demonstrate value through direct action rather than resume submissions. (25:46) Jason's advice was explicit: "email the CEO of the company and redesign their landing page" rather than going through HR channels. With companies finding it easier to build AI agents than train new employees, job seekers must show initiative, creativity, and immediate value creation. This requires developing resilience, taking calculated risks, and proving capabilities through concrete work rather than credentials. The traditional campus recruiting and training programs are disappearing, making self-directed skill development essential.
Statistics & Facts
- McKinsey saved 1.5 million hours in search and synthesis tasks in the last year through AI implementation, while simultaneously growing their client-facing workforce by 25% and reducing non-client-facing staff by 25%. (09:18)
- McKinsey currently has 40,025 humans and 40,025 personalized agents as of the recording date, expecting to reach parity between humans and AI agents by the end of 2026. (29:59)
- The return on investment for employee skills training has shrunk by half over the last 30 years, dropping from seven years to approximately 3.6 years, and continuing to decrease as the pace of change accelerates. (29:35)
