Special Guest TBA

Special Session TBA

Every economic shift comes with a price tag, and this one is being paid in real time. The World Economic Forum projects 92 million jobs will disappear worldwide by 2030 as AI and automation take hold, while 170 million new ones get created in their place. That nets out to a 78 million job gain on paper. On the ground, the picture looks different. The International Monetary Fund estimates AI will touch nearly 40% of jobs globally, and Goldman Sachs now puts the pace of net U.S. job loss from AI at roughly 16,000 a month, concentrated in administrative, entry-level, and routine roles. 

A net gain at the national level can still mean a net loss at the kitchen table. The jobs disappearing tend to cluster in specific occupations and specific ZIP codes; the jobs replacing them tend to require different skills, different credentials, sometimes a different city altogether. Communities don’t experience economic transitions as an average. They experience them as a layoff notice, a stalled paycheck, a kid who has to explain to a parent why the trade they trained for isn’t hiring anymore. 

What do communities actually need to adapt, not in theory but in practice: retraining, capital, broadband, something else entirely? Who bears responsibility for managing this transition — employers, government, workers themselves? Which communities are already finding a way through, and what can the rest of us learn from them? Does the net job math even matter to a family living through the net loss? What would it take to make this transition feel less like something that happens to people and more like something they have a hand in shaping?

Ask three firms how much money is about to change hands in America and you’ll get three different answers, each in the trillions. Cerulli Associates puts the number at $124 trillion moving from older generations to heirs and charity by 2048. Visa’s economists, using a more conservative model that strips out liabilities and the top 1%, put it at $36 trillion. That’s not a rounding error. That’s an $88 trillion gap between two credible estimates of the same event. 

The “Great Wealth Transfer” has become shorthand for the idea that an entire generation is about to inherit its way into financial security. Baby boomers do hold roughly $93 trillion in assets today, more than Gen X and millennials combined. But most of that wealth sits with a small slice of boomers. The Federal Reserve reports the top 1% of U.S. households now hold 31.7% of the country’s wealth, the highest share on record since it started tracking in 1989, worth about as much as the bottom 90% combined. 

If the wealth transfer is real for some families and a myth for most, then treating it as a universal event, in financial planning, in public policy, in how young people picture their own future, sets a lot of people up to plan around money that was never coming their way. 

Is the Great Wealth Transfer a broad-based shift or a story about a small number of very wealthy families? Who is actually positioned to inherit meaningful wealth, and who is being sold a false sense of what’s coming? What should families do differently if they can’t count on an inheritance to secure their future? How should financial institutions, employers, and policymakers respond to a transfer that concentrates rather than distributes? What would it take to make sure the wealth that does move creates opportunity beyond the households receiving it?

Women now own 14.5 million businesses in the United States, nearly 40% of all companies in the country, employing almost 13 million people and generating $3.3 trillion in revenue a year. That is not a rounding error in the economy. That is a structural shift. And yet the gap tells its own story: closing the revenue gap between women-owned and men-owned businesses would add an estimated $10.2 trillion annually to the economy. 

The gap runs deeper inside the numbers. The average minority women-owned business generates $65,800 in annual revenue, compared to $218,800 for non-minority women-owned businesses. Equalizing that gap alone is projected to add $832 billion to the economy and create four million jobs. Access to capital remains the sharpest edge of the problem: the average loan for a women-owned business is roughly half the size of the average loan for a male-owned one. 

Every dollar that doesn’t reach a woman-owned or minority-owned business is a dollar that doesn’t hire a neighbor, doesn’t open a second location, doesn’t pass down to the next generation. Who builds the future isn’t a rhetorical question. It’s a capital allocation question, and right now the answer skews narrow. 

What is actually standing between women and minority entrepreneurs and the capital they need — the lenders, the underwriting models, the networks, or all three? How much of the revenue gap comes down to industry choice versus access to growth capital once a business is already up and running? What are the models, public, private, or philanthropic, that are proving they can close this gap at scale? How should corporate procurement and venture capital change to reflect who is actually building businesses in this country? What would it look like for this to stop being framed as an equity issue and start being treated as an economic growth strategy?

The top 1% of American households now hold 31.7% of the country’s wealth, the highest share on record since the Federal Reserve began tracking it in 1989. That’s roughly $55 trillion, about equal to what the bottom 90% of the country holds combined. The top 10% of earners now account for 49.2% of all consumer spending, up from 43.2% just five years ago. The engine of the American economy increasingly runs on a smaller and smaller number of households. 

Wages tell the same story from a different angle. Since 1979, wages at the bottom of the income ladder have grown 28.6%. Wages at the top have grown 64.1%, more than double the pace. This isn’t a story about people not working hard enough. It’s a story about where the gains from decades of growth actually landed. 

Capitalism built the most productive economy in human history. It also, left unexamined, concentrates the rewards of that productivity in fewer and fewer hands. The question in this room isn’t whether markets work. It’s whether the version of capitalism we’ve built is still delivering for the people doing the work. 

Is this level of concentration a sign the system is broken, or a sign it’s working exactly as designed — and if it’s the latter, what needs to change about the design? What would it actually take to grow the economy in a way that lifts wages at the bottom faster than wealth at the top? Where has capitalism been rewritten before, in this country or elsewhere, and what worked? Is the answer more regulation, different ownership models, different tax policy, or something none of us are talking about yet? What role should business leaders themselves play in changing an outcome that’s currently working in their favor? 

Where you’re born and the color of your skin still predict how long you’ll live in this country, and the gap is widening, not closing. A landmark study in The Lancet found the life expectancy gap between the highest- and lowest-performing populations in America has grown to 20.4 years, up from 12.6 years in 2000. AARP projects disparities in life expectancy alone will cost the U.S. economy $1.6 trillion in 2030, about 5.1% of GDP, and reduce consumer spending by $1.1 trillion that year alone. 

The gap isn’t abstract, and it isn’t only racial. Rural counties are almost universally short on specialists — cardiology, endocrinology, rheumatology. A five-month wait for a specialist is a different problem than a five-minute one, and it’s the reality for millions of Americans outside major metro areas. Underserved populations carry a 40% higher rate of chronic conditions like diabetes and hypertension, and Black adults face a 23% greater risk of premature death from conditions that are preventable. 

AI is already extending specialist-level insight into places that have never had it: reading a retinal scan for diabetic eye disease in a rural clinic, flagging a cardiac risk a primary care doctor might miss, running a diagnostic tool on a phone with no internet connection required. The FDA has now authorized more than 1,357 AI-enabled medical devices, more than double the number from 2022. But AI trained on the same skewed data that produced today’s disparities can automate them at scale just as easily, and insurers are already using AI to deny claims faster than underresourced providers can afford to challenge them. Whether AI closes this gap or hard-wires it into the system depends on choices being made right now: what data trains these tools, who deploys them, and who’s in the room when they’re built. 

Is the real health gap primarily about access, about bias in the system, or about the underlying economics of where care gets built? Where has AI already proven it can extend real care to a population that didn’t have it before? What guardrails need to be in place before AI in healthcare does more good than harm, and who’s responsible for enforcing them? How do we keep AI from repeating the pattern where new health technology reaches the well-resourced first and the underserved last? What would it take to close a 20-year life expectancy gap within a generation, and is that even a realistic goal?

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