The echo of rapid technological advancement reverberated through CES 2026, a stark reminder that the skills of yesterday might not secure the jobs of tomorrow. In a landscape increasingly sculpted by artificial intelligence, the mantra of "learn once, work forever" is rapidly becoming an obsolete relic.

The shift was palpable during a live recording of the All-In podcast at the conference, where Jason Calacanis engaged in a thought-provoking discussion with Bob Sternfels, Global Managing Partner of McKinsey Company, and Hemant Taneja, CEO of General Catalyst. Their conversation, a focal point of the conference, centered on the profound ways AI is not only reshaping investment strategies but also fundamentally altering the very fabric of the workforce.

Taneja, a seasoned venture capitalist, painted a picture of unprecedented growth in the AI sector. He highlighted the meteoric rise of companies like Anthropic, a General Catalyst portfolio company, which catapulted from a $60 billion valuation last year to a staggering valuation in the hundreds of billions this year. This trajectory, he argued, signals the dawn of a new era, one where trillion-dollar companies are no longer a distant dream but a tangible possibility. "We are on the verge of seeing a new wave of trillion-dollar companies," Taneja asserted, pointing to Anthropic, OpenAI, and a select few others as prime contenders.

Calacanis, ever the inquisitive host, pressed Sternfels and Taneja on the driving forces behind this explosive growth. Sternfels acknowledged that while many companies are experimenting with AI products, a significant portion of the non-tech sector remains hesitant about fully embracing the technology. This reluctance, he suggested, stems from a combination of factors, including concerns about implementation costs, data security, and the potential displacement of human workers.

The conversation underscored a critical point: the need for continuous learning and adaptation in the age of AI. The traditional model of acquiring a fixed set of skills and applying them throughout one's career is no longer sufficient. Instead, individuals must embrace a mindset of lifelong learning, constantly updating their knowledge and abilities to remain relevant in a rapidly evolving job market. This requires not only acquiring new technical skills, such as proficiency in AI programming languages like Python and frameworks like TensorFlow, but also developing soft skills like critical thinking, problem-solving, and creativity, which are essential for navigating the complexities of an AI-driven world.

The implications of this shift extend far beyond individual workers. Companies, too, must adapt their training and development programs to equip their employees with the skills they need to thrive in the AI era. This may involve investing in internal training programs, partnering with educational institutions to offer specialized courses, or even creating entirely new roles focused on AI implementation and management.

Looking ahead, the future of work will likely be characterized by a dynamic interplay between humans and AI. AI will automate routine tasks, freeing up human workers to focus on more creative and strategic activities. However, this transition will require careful planning and execution to ensure that workers are not left behind. As Sternfels noted, the key is to view AI not as a replacement for human workers, but as a tool that can augment their capabilities and enhance their productivity. The companies that succeed in harnessing the power of AI will be those that prioritize human capital and invest in the ongoing development of their workforce. The era of "learn once, work forever" is indeed over, replaced by a new paradigm of continuous learning and adaptation.