July 14, 2026: Goldman Sachs' warns that the real AI productivity payoff may not arrive until 2030 at the earliest, because companies are buying the technology faster than they are redesigning work. Then I get into the split among leading economists over whether AI needs new institutions and guardrails now, or whether early governance could slow progress. Finally, I look at IBM's major market shock and why it shows that even companies selling AI transformation can struggle when customer behavior changes faster than the organization can move.

I talk with Ron Friedman, award-winning social psychologist and bestselling author of The Best Place to Work and Super Teams, about what high-performing teams actually do differently. We get into why great teams avoid unnecessary meetings, how teammates make each other better, why feedback usually fails, and how AI is changing collaboration, curiosity, communication, and accountability at work.

July 10, 2026: Axios warns about AI's new class divide: the haves, the have-nots, and the "no-nots" who are already being shaped by AI without realizing it. Then I get into new Indeed and Business Insider data showing that AI is moving into job titles far beyond tech, from physical therapy to legal, finance, marketing, and management. The bigger issue is not just who has access to AI. It's who knows how to use it, who is being quietly managed by it, and whether leaders use AI to make people better or slowly de-skill them.

July 9, 2026: OpenAI released its new GPT 5.6 lineup, Elon Musk's xAI launched Grok 4.5, and GPT Live showed where voice-based AI may be heading next. Then I get into Uber's Agentic Pods, where the company is embedding AI-proficient engineers inside legal, finance, and HR even as leadership admits it still can't prove the ROI. Finally, I look at the Brown University AI cheating scandal, where students averaged 96 on a take-home midterm and then collapsed to 48 on an in-person final, and why this should worry every leader thinking about AI, skill, and judgment.

I talk with Yolanda Seals-Coffield, PwC US Chief People and Inclusion Officer, about how PwC is preparing 80,000 people for an AI-enabled future. We get into PwC's approach to democratizing AI access, building responsible AI habits, measuring adoption beyond token usage, and creating learning that happens directly in the flow of work. Yolanda also shares how PwC is rethinking entry-level talent, human skills, and career development as AI changes the work new graduates are expected to do.

July 3, 2026: Tesla is capping employee AI spending at $200 a week after some engineers reportedly burned through thousands of dollars in tokens, showing that "free" AI was never really free. Then I get into the rise of social offloading, where people use AI not just to think for them, but to handle difficult messages, feedback, and human interactions.

July 2, 2026: Microsoft is putting $2.5 billion and 6,000 employees behind a new group designed to help customers actually use AI inside their businesses, following a similar move from Amazon. Then I get into why former chief AI officer Sol Rashidi fired half of her AI agents after spending more time babysitting them than getting work done. I also look at why administrative assistants may become more valuable in the AI era when they know how to use the tools, manage context, and apply judgment.

July 1, 2026: Companies are discovering that AI agents can drive token usage far beyond what they budgeted for, forcing leaders to bring cloud-style cost controls into AI spending. Then I get into Ford bringing back 300 veteran quality inspectors and engineers after its AI-driven quality checks missed what experienced humans could catch. Finally, I look at Palantir CEO Alex Karp's CNBC warning about token pricing, customer data, model control, and why more companies may start rethinking how much of their AI strategy they want to outsource.

June 30, 2026: New research from Ramp Economics Lab and Revelio Labs shows that companies spending the most on AI are not shrinking the fastest. They are actually growing headcount faster, including in entry-level roles many assumed would disappear first. Then I get into Amazon Web Services' $1 billion push to build a new unit of embedded AI engineers, sending teams directly into customer organizations to help turn AI pilots into real work. The bigger story is that AI is not simply replacing jobs. It is changing which jobs grow, which skills matter, and where the real bottleneck is.

I talk with Mike Thomson, President and CEO of Unisys, about the real state of AI inside companies. We get into why some organizations are using AI as cover for layoffs, why the backlash against data centers is often built on incomplete information, and why enterprise AI adoption will take years instead of weeks. Mike also explains why technical debt, messy data, governance, security, token costs, and workforce readiness are the real barriers leaders need to understand before they assume AI can simply replace people at scale.