The old career script — study hard, get a degree, then coast for 40 years — just got shredded on stage at CES 2026.
On a live taping of the All-In podcast, co-host Jason Calacanis pressed Bob Sternfels, global managing partner at McKinsey & Company, and Hemant Taneja, CEO of General Catalyst, on how AI is rewriting both investing and work.
Their verdict: AI is moving so fast that “learn once, work forever” simply doesn’t map to reality anymore.
AI is minting giants at unprecedented speed
Taneja argued that the pace of value creation around AI is unlike anything the industry has seen.
He pointed to his own portfolio to make the case:
- Stripe took about 12 years to reach a $100 billion valuation.
- Anthropic, another General Catalyst bet, jumped from a $60 billion valuation last year to what Taneja described as a “couple hundred billion dollars” this year.
“The world has completely changed,” he said, adding that we’re on the verge of a new wave of trillion‑dollar companies. That’s “not a pie-in-the-sky idea with Anthropic, OpenAI, and a couple of others,” he argued.
Inside the boardroom: CFO vs. CIO
Calacanis asked what’s actually driving this explosive growth when so many non‑tech companies are still dabbling rather than fully committing to AI.
Sternfels said CEOs keep running into a simple but brutal dilemma: “Do I listen to my CFO or my CIO right now?”
- CFOs look at early pilots and see thin or unclear ROI, so they push to slow down spending.
- CIOs insist it’s “crazy” not to adopt AI now because “we’ll be disrupted” if they wait.
That tension — between protecting the quarterly numbers and betting on long‑term competitiveness — is one big reason enterprise AI adoption is uneven, Sternfels suggested.
Young workers: judgment beats tasks
Calacanis raised the anxiety that many graduates feel: if AI can do entry‑level work, what’s left for humans just starting out?
Sternfels’ answer: the work changes, but the bar for distinctly human skills goes up.
He argued that while AI models can now take on a wide range of tasks, sound judgment and creativity are what humans have to bring to the table to stay relevant in an AI‑infused workplace.
Taneja pushed the point further. The old life plan is broken, he said:
“This idea that we spend 22 years learning and then 40 years working is broken.”
In his view, “skilling and re‑skilling” will have to become a lifelong habit, not a phase you finish when you leave university.
Calacanis agreed that in a world where it might be faster to spin up an AI agent than to train a junior hire, people will have to differentiate in other ways. “To stand out, you’re going to have to show chutzpah, drive, passion,” he said.
How McKinsey is reorganizing around AI
Sternfels also offered a concrete look at how a blue‑chip firm is adapting from the inside.
By the end of 2026, he expects McKinsey to have roughly as many personalized AI agents as employees. But that doesn’t mean a mass layoff triggered by automation, at least not in his telling.
Instead, McKinsey is reshaping its workforce mix:
- Increasing the number of people who work directly with clients by 25%
- Reducing back‑office roles by the same 25%
The message: AI won’t just delete jobs, it will reallocate them — and reward workers who sit closer to customers and complex decisions.
The next career playbook
Threaded through the conversation was a clear signal to both founders and workers:
- For startups and investors, AI is compressing timelines and supercharging valuations for the winners.
- For employees, especially younger ones, the premium shifts to adaptability, continuous learning, and human judgment.
The panel didn’t offer comforting guarantees. But it did offer a roadmap: treat learning as an ongoing product update, not a one‑time launch, and assume you’ll be reskilling as often as your tools are.
