AI Is Creating Jobs We Never Imagined

For the past year, much of the AI and work conversation has focused on what we might lose: entry-level jobs, managers, and repetitive tasks. But a compelling shift is underway: what if AI is not just replacing jobs, but inventing new ones?

In a deeply reported piece for ​The New York Times Magazine​, Robert Capps lays out 22 new AI-era jobs organized around three emerging themes: trust, integration, and taste.

• Trust: AI may automate decisions, but it cannot shoulder accountability. That’s where roles like AI auditors and trust authenticators step in, people who blend ethics, technical insight, and judgment to ensure systems are responsible, accurate, and explainable. These roles are already critical in healthcare, finance, and law.
• Integration: Technology alone isn’t transformation. To make AI work at scale, businesses need new types of integrators, AI plumbers, trainers, and even personality directors. These professionals connect systems to strategy, ensuring tools are usable, aligned, and valuable within real-world operations.
• Taste: AI levels the playing field, but it’s still humans who set the vision. Creative, taste-driven roles—from story designers to world builders—will shape what stands out in a world flooded with machine-made content. Even in HR and education, the ability to make meaningful decisions will become a defining skill.

As Erik Brynjolfsson notes, we are all becoming CEOs of our own AI teams. These new roles are not just technical, they’re emotional, ethical, and entrepreneurial.

I keep a close eye on what our Future Work Experts are thinking, saying, and questioning. I break down the key conversations and brainstorm practical steps we can take to move forward.

This week:

EMPLOYEE EXPERIENCE

​Phil Kirschner: The EX That Proves ROI​

• Phil Kirschner reveals that even top EX experts struggle to link employee experience initiatives to measurable business impact—an insight he surfaced through a single, unanswered question at Future of Work USA.
• He reframes EX using McKinsey’s Organizational Health Index, urging teams to design backward from business strategy and build people practices that enable capabilities like M&A integration, frontline enablement, and onboarding ROI.
• He cites real examples: PepsiCo’s facility upgrades, Campari’s culture codification, and Wix’s onboarding redesign, to show how EX can drive retention, time-to-productivity, and deal success when connected to strategy, not just sentiment.

📝 Prompt: Before launching your next EX initiative, complete this sentence:“If we improve [specific EX], we expect [specific outcome], because [business logic].” Then test if your exec team would sign off.

​👉 Read how silence revealed the biggest blind spot in EX.​

CHANGE MANAGEMENT

​Brian Elliott: Flexibility and AI Share One Playbook​

• Brian Elliott reveals that only 25% of managers are trained to lead distributed teams, and just 22% of firms have an AI adoption plan, despite near-universal urgency from executives.
• He outlines a four-pillar framework—Talent Strategy, Outcomes-Based Management, Team-Centered Approach, and Learning Culture—as the shared path to mastering both hybrid work and AI transformation.
• Companies like Airbnb, Udemy, Atlassian, and Zapier succeed not by policy or tech, but by embedding capability-building, team trust, and outcome focus into their operating system.

📝 Prompt:  Run a solo scorecard: Rate yourself (1–4) across the four pillars—Are you clear on your “why”? Do you focus on outcomes over activity? Are you co-creating with your team? Are you learning out loud?

​👉 Apply the four pillars to your next transformation.​

LEAD WITH AI

​Daan Van Rossum: AI-Smart or AI-Stupid? You Choose​

• Daan warns that over-relying on AI tools like ChatGPT risks creating “cognitive debt”—a silent spiral of skill decay, memory loss, and disengagement.
• Citing MIT Media Lab and Microsoft studies, he shows how passive AI use weakens executive brain functions and retention, while active, structured use amplifies thinking and learning.
• He urges leaders to shift from “copy-paste culture” to intentional workflows: start with human insight, challenge AI outputs, and embed reflection rituals to make AI a thinking partner, not a thinking replacement.

📝 Prompt: Audit your own AI habits: Are you passively accepting answers or actively thinking with the tool? Run a simple test—turn off AI and recreate the output from memory or logic. Can you explain it without help? What you forget, you never learned. What you question, you truly understand.

​👉 Read how to stop AI from dulling your edge.​

🔥 QUICK HITS

​(AI EMPOWERMENT) Antony Slumbers: Everyone’s a Developer Now​

• Antony Slumbers reveals that AI microtools like Lovable and Cursor are dismantling IT gatekeeping by enabling non-coders to build custom software with plain English, shifting competitive advantage from technical talent to problem-spotters.
• He shows how this “microsoftware” revolution addresses the long tail of previously uneconomical workflow needs, redefining how work is done and breaking the link between productivity and physical office space.
• He argues that this shift is not just about automation, but a redistribution of power, from centralized IT to individuals with domain expertise, who now have the tools to create, solve, and innovate on their own terms.

📝 Prompt: Encourage everyone to build one solution using a no-code or AI microtool, just once. Start small: automate a daily task, solve a workflow pain point, or simplify a personal process. Experience what it feels like to create without asking for permission.

​👉 Explore how microtools are reshaping power at work.​

​(AI ADOPTION) Anthony Onesto: Hackathons Fix Your AI Culture Gap​

• Anthony Onesto reveals that most AI resistance isn’t technical, it’s human. By treating adoption as a culture challenge, he shows how hackathons cut through fear, surface real use cases, and build confidence from the ground up.
• Backed by data, he notes developer adoption jumps 20% post-hackathon, and 80% of Fortune 100 firms run them regularly to turn AI curiosity into capability.
• But the magic isn’t the prototypes, it’s what leaders do next: funding real follow-through, rewarding grassroots innovation, and proving that AI isn’t a mandate, it’s a movement.

📝 Prompt: Plan your own “AI Week” with a hackathon at the center. Invite cross-functional teams to solve one real workflow challenge using AI.

​👉 Read how hackathons turn AI fear into momentum.​

​(DATA MANAGEMENT) Jason Averbook: Digital Debt Is Starving Your AI​

• Jason Averbook argues that messy, unstructured, tribal data is the real threat to GenAI adoption, and calls it the “digital debt” we’ve ignored for too long.
• He debunks the myth that AI will clean up the chaos, warning that “AI doesn’t fix your mess, it scales it,” with stats showing poor data quality costs U.S. firms trillions annually.
• Jason shows how leaders can flip the script by using GenAI now to tag, summarize, and structure legacy content—and shift teams from passive storage to active data stewardship.

📝 Prompt: Map your team’s “digital junk drawer.” Try one GenAI tool this week to clean, structure, or summarize buried documents—and start building a culture of data readiness.

​👉 Read why GenAI needs better inputs, starting with yours.​

I track what’s worth your attention—bringing you the news and updates that matter most to how we work, lead, and grow.

This week:

AI & PEOPLE

​AI’s Hardest Problem: People, Not Tech​

Microsoft CEO Satya Nadella emphasizes that the hardest part of AI transformation is not the technology itself, but changing how people work.

As AI tools like agents reshape workflows, roles are evolving. For example, LinkedIn is merging product design, engineering, and management into “full-stack builder” roles. Satya stresses that change management is the key bottleneck, as companies need to redesign job scopes, team structures, and daily processes.

While some leaders (like Jensen Huang and Reid Hoffman) see AI as a creative unlock, others (like Dario Amodei) warn it may displace up to 50% of entry-level white-collar jobs.

Leaders from Amazon to Microsoft agree: AI must be embedded across all levels, and everyone—from executives to new hires—must learn, experiment, and adapt quickly.

📝 Prompt:
When introducing AI or changing workflows, psychological safety must come before adoption. Use these questions to open the conversation across any team or role:
1. What concerns or unspoken fears do you have about how AI might change your role or value to the team?
2. Do you feel safe sharing when you’re unsure about how to use new tools or workflows, and if not, what would help create that space?
3. When was the last time you felt genuinely heard during a period of change, and how can we replicate that now?

MILLENNIALS PRESSURE

​Millennial Burnout Hits Breaking Point​

Millennials, now largely in middle management, report the highest burnout rates at 66%, driven by a “perfect storm” of career pressure, caregiving duties, and economic strain.

They face the dual challenge of managing up and down during massive workplace shifts like AI adoption.

While many companies offer mental health resources, poor communication and unclear access dilute their impact.

Research shows burnout is often rooted in personal stress and lack of social support—not just work overload—highlighting the urgent need for human connection, proactive check-ins, and better leadership support during times of change.

📝 Prompt: Block 30 minutes this week to have a non-performance check-in with one Millennial manager on your team. Ask:
“What’s been weighing on you lately—at work or beyond?”
“Where do you feel most supported right now, and where do you feel alone?”
“What would help you feel more connected and energized in the next 2 weeks?”

LEADERS IN TRANSFORMATION

​Paypal: Building Bold Leaders​

During PayPal’s transformation into a broader commerce platform, Chief People Officer Isabel Cruz introduced leadership and career frameworks to align a changing workforce.

She launched three leadership principles—people first, customer-centered problem solving, and winning together—supported by the BOLD framework to build traits like vision, collaboration, and self-investment.

Her team also codified career paths with clear skills and proficiency levels, making growth transparent and accessible, regardless of managerial support. AI-powered opportunity matching is next.

By embedding these values top-down, PayPal is uniting performance, culture, and transformation into one coherent people strategy.

📝 Prompt: What if your team had complete clarity on what great leadership looks like and how to grow, regardless of their manager? Start by mapping one career path in your org:
- What skills define each stage?
- How is progress currently tracked and supported?
- Where can AI help personalize the journey?

GRADUATE JOB MARKET

​Why 2025 Grads Are Struggling​

Graduates in 2025 are facing the toughest job market in over a decade, with a 5.8% unemployment rate for degree holders aged 22–27—the highest since 2012 (excluding the pandemic).

A mismatch between high education levels and available roles, combined with economic uncertainty, tariffs, and early AI adoption, is reducing entry-level hiring—especially in white-collar fields like IT, law, and finance.

Despite long-term earnings advantages, today’s grads struggle to land jobs even with internships and strong résumés. AI may be accelerating this trend, with CS grads seeing an 8% drop in employment since 2022, and some firms asking teams to justify human hires.

Still, economists point to Fed rate hikes and tech overexpansion as primary drivers of the hiring slowdown—not AI alone.

📝 Prompt: Rewrite one job post for graduates this week to clarify expectations, highlight available support or training, and name AI tools they’ll use.

LEADERSHIP STYLE

​NVIDIA CEO: ‘Stick to Your Knitting’​

Nvidia CEO Jensen Huang defies the old leadership advice to “stick to your knitting,” showing that adaptability—not insularity—is the defining trait of modern leadership.

At the Yale CEO Leadership Forum, Huang described how he discarded business plans twice, boldly pivoting Nvidia—from graphics chips to AI infrastructure, through step-by-step reinvention. Nvidia’s revenues surged from $27B to $130B in two years, fueled by accelerated computing demand post-ChatGPT.

His leadership is rooted in vision, humility, and the courage to reimagine the company multiple times, even at the height of its success.

As IBM’s Arvind Krishna put it, Huang’s brilliance lies not just in foresight—but in the fortitude to lead deep transformation.

📝 Prompt: Hold a 45-minute leadership session this month to ask:
- What would we do differently if we started this business today?
- Where might reinvention, not optimization, be the wiser move?
- What bold pivot are we avoiding—and why?

🔥 QUICK READ: World Economic Forum - Summer Davos 2025

​🗓️ Day 1 – Growth, AI, and Global Fragmentation​

• AI flattens hierarchies – Workera CEO: Orgs shift to skills-based pay.
• Trade needs logic, not tariffs: Focus moves to labor, immigration, and partnerships.
• China’s energy play = security strategy: Renewables surge as a national priority.
• Gender gap = 123 years – more inclusion = faster growth.
• Emerging tech converges: Breakthrough innovations now collide across AI, bio, energy.

​🗓️ Day 2 – Asia’s Moment, Energy Shift, and Climate Finance​

• Asia leads, but seeks balance: PMs of Singapore and Viet Nam stress regional cooperation, resilience, and multilateralism.
• Africa is underfunded in climate finance: Despite $2T in clean energy investment, Africa gets only 2%.
• Energy = Growth: Transition isn’t just about climate, it’s about GDP, innovation, and leapfrogging.
• AI agents disrupt work: From digital twins to AI-managed teams, leadership now means managing machines.
• Emotion drives action: Ecological artist Thijs Biersteker: data must feel real to drive climate engagement.

​🗓️ Day 3 – China’s Strategy, AI+ Economy, and Talent Velocity​

• AI+ is China’s edge: A full-stack approach—data, compute (Huawei), and models (DeepSeek)—is powering rapid industrial AI transformation.
• US-China ‘decoupling’ is an illusion: Despite political tension, their economies remain deeply interconnected.
• Entrepreneurship needs structure: Innovation thrives where policy, talent, and infrastructure align.
• AI skills = short shelf life: Continuous learning is now essential, not optional.
• Geopolitics reshaped by tech: The next battleground is development models and open-source ecosystems.