AI Reshapes Work: “Learn Once, Work Forever” Is Over
At CES 2026, a clear message emerged from tech leaders and investors alike: the old model of education and employment is obsolete. In a live taping of the All-In podcast, executives from McKinsey & Company and General Catalyst declared that AI is not just changing how we work—it’s redefining the very foundation of careers. With trillion-dollar valuations now within reach for AI firms and companies torn between cautious finance teams and urgent tech leaders, one thing is certain: continuous learning is no longer optional—it’s survival.
The AI Gold Rush Is Real—and Accelerating
Hemant Taneja, CEO of venture powerhouse General Catalyst, didn’t mince words when describing AI’s breakneck pace. “It took Stripe 12 years to hit a $100 billion valuation,” he noted. “Anthropic went from $60 billion to a few hundred billion in just one year.” This explosive growth signals a new economic era, where AI-native companies could soon join—or even surpass—the market caps of today’s tech giants. Taneja believes we’re on the cusp of seeing multiple trillion-dollar enterprises born from this wave, naming Anthropic and OpenAI as prime candidates.
CEOs Are Torn Between Finance and Tech Teams
Despite the hype, many established companies remain stuck in implementation limbo. Bob Sternfels, Global Managing Partner at McKinsey, revealed that CEOs are caught in a tug-of-war between their CFOs and CIOs. “CFOs see little ROI so far and want to wait,” Sternfels explained. “But CIOs are saying, ‘If we don’t move now, we’ll be disrupted.’” This internal tension is slowing enterprise adoption—even as startups race ahead. The result? A widening gap between AI-first firms and legacy businesses playing catch-up.
The “Learn Once, Work Forever” Model Is Dead
Perhaps the most urgent shift lies in how we prepare for work itself. Sternfels and Taneja agreed: the traditional path—go to school, land a job, retire decades later—is no longer viable. “The era of ‘learn once, work forever’ is over,” Sternfels declared. Rapid AI advancements mean skills depreciate faster than ever. What was cutting-edge last year might be obsolete by next quarter. For workers, especially young professionals, this demands a mindset shift from static credentials to lifelong upskilling.
Entry-Level Jobs Are at a Crossroads
Jason Calacanis raised a pressing concern: AI could automate many entry-level roles that once served as on-ramps for new graduates. Tasks in coding, customer support, data entry, and even basic legal research are increasingly handled by AI agents. “Some people are looking at AI and they’re scared,” Calacanis observed. The executives acknowledged the risk but offered a counterpoint: while AI may replace certain tasks, it also creates new opportunities—for those ready to adapt.
Young Workers Must Embrace “Learning Agility”
So what should students and early-career professionals do? According to Taneja, the answer isn’t to avoid AI—but to master it. “Become fluent in AI tools, understand how they work, and learn how to direct them,” he advised. Sternfels added that “learning agility”—the ability to quickly acquire and apply new knowledge—will be the most valuable skill in the 2030s workplace. Degrees still matter, but real-time problem-solving and adaptability matter more.
Companies Must Invest in Reskilling—Now
The responsibility doesn’t fall solely on individuals. Sternfels urged companies to treat workforce transformation as a strategic priority, not a cost center. “Waiting until your competitors are fully AI-enabled is too late,” he warned. McKinsey’s data shows that firms investing in reskilling see higher retention, faster innovation cycles, and better ROI on AI deployments. The most successful organizations won’t just deploy AI—they’ll embed it into their culture of learning.
AI Won’t Replace People—But People Using AI Will
A recurring theme in the discussion was augmentation over replacement. Taneja emphasized that AI’s greatest value lies in amplifying human potential. A marketer using AI to analyze customer sentiment can craft more resonant campaigns. A developer leveraging AI co-pilots can build faster and safer code. The winners won’t be those who resist AI, but those who wield it with purpose and creativity.
The Rise of “Hybrid” Career Paths
We’re also seeing the birth of entirely new job categories that blend domain expertise with AI literacy. Think “AI ethicists in healthcare,” “prompt engineers for finance,” or “AI-augmented designers.” These hybrid roles demand both deep industry knowledge and fluency in AI systems. For career changers and students alike, this opens exciting, interdisciplinary paths that didn’t exist five years ago.
Education Systems Are Playing Catch-Up
Unfortunately, most universities and vocational programs haven’t kept pace. “The education system is still optimized for the industrial era,” Taneja lamented. He called for closer collaboration between academia, industry, and governments to build modular, stackable learning pathways—micro-credentials, bootcamps, and on-the-job training that evolve with market needs. The goal? Make reskilling as seamless as updating an app.
What This Means for the Future of Work
The bottom line from CES 2026 is clear: AI isn’t coming—it’s already here, and it’s rewriting the rules. Companies that hesitate risk obsolescence. Workers who cling to static skill sets may find themselves sidelined. But for those who lean into continuous learning, AI offers unprecedented leverage. As Sternfels put it: “The future belongs to the curious.”
A New Social Contract Is Emerging
Finally, the conversation hinted at something deeper—a need for a new social contract around work, learning, and equity. If AI drives massive productivity gains, how do we ensure those benefits are shared? Taneja and Sternfels stopped short of policy prescriptions but agreed that inclusive access to AI education is non-negotiable. The alternative isn’t just economic inefficiency—it’s societal fracture. The time to act is now, before the gap widens further.
