By Paula Pant
Behavioral economist Etinosa Agbonlahor joins us to discuss “money scripts” — the unconscious beliefs we inherit or develop about finances.
Agbonlahor, CEO of Decision Alpha and former Director of Behavioral Science Research at Fidelity Investments, is the author of “How to Talk to Your Parents About Money.”
She studied financial management at Cornell University and […]
By Paula Pant
Arielle’s head is spinning from the seemingly contradictory advice she hears about the best investments to hedge against inflation and a possible recession. What’s she missing?
Dave is curious about private investments after listening to a recent First Friday episode. What are they, and should he consider them for his portfolio?
Abbey is stoked about the raise she negotiated for her first job out of school. But she’s worried about liability risk related to her new position. How does she protect herself?
Former financial planner Joe Saul-Sehy and I tackle these three questions in today’s episode.
By Paula Pant
Picture this: you’re 26 years old, fresh out of Wharton, and you decide to start a business with two friends. You spend years building a digital marketing firm that eventually works with Dollar Shave Club and Madison Reed. You bootstrap the entire thing without taking a dime of venture capital funding.
That’s exactly what one Wharton graduate did — and her story represents the reality of entrepreneurship that most people never hear about.
Lori Rosenkopf, a management professor at Wharton Business School and head of Venture Labs, joins us to shatter the biggest myths about starting a business. The Mark Zuckerberg college dropout story? It’s not just rare — it’s misleading.
Research shows that the most successful entrepreneurs, those in the top 0.1 percent of venture-backed firms, average late 30s to early 40s when they start their companies. Many continue launching businesses into their 50s and 60s.
Your age and corporate experience isn’t holding you back from entrepreneurship — it’s actually giving you an advantage.
Rosenkopf breaks down seven different types of entrepreneurs, from disruptors who overturn entire industries to bootstrappers who build profitable businesses using their own resources. You’ll hear about a founder who disrupted the hair color industry in her 50s with Madison Reed, and a banker who built an entire financial services division inside Square.
We cover the rise of direct-to-consumer brands in 2013, why 80 percent of entrepreneurs are bootstrappers, and how artificial intelligence is creating new opportunities for people to start businesses without massive upfront investments.
Rosenkopf explains her “six Rs” of entrepreneurial thinking: reason, recombination, relationships, resources, resilience, and results. She argues that most people already think entrepreneurially without realizing it — even parents who optimize their family routines are solving problems through innovation.
We explore the world of “intrapreneurs” — people who build new businesses within established companies — and discuss acquisition entrepreneurship, where people buy existing small businesses instead of starting from scratch.
Whether you want to start a side hustle, position yourself for a promotion, or eventually launch your own company, Rosenkopf’s framework shows multiple paths to creating value through innovation.
By Paula Pant
Paul is worried the private equity investment he’s about to make could be a scam. How can he do his due diligence and stay protected when there’s a shortage of reliable information?
Rob is questioning the purpose of a bond allocation in his eight-figure investment portfolio. Is he on to something, or is there a legitimate case to add them?
Dan can retire in a few years, but he’s itching to do it now. Would buying a business be the key to unlocking an earlier exit from his W2?
Former financial planner Joe Saul-Sehy and I tackle these three questions in today’s episode.
Enjoy!
By Paula Pant
#632: There are 10 conversations that a person should have at work in order to do a better job, have better relationships at work, and make more money.
Melody Wilding, Professor of Human Behavior at Hunter College, joins us to talk about how you can get the most out of your […]
By Paula Pant
Jason’s analysis of his retirement plan shows that the simple path beats the efficient frontier. Is he right or is he missing something?
Minerva is worried about the impacts of tax inefficiency to her wealth. Are her investments properly located?
Scott feels frozen because he doesn’t understand the nuances of the efficient frontier. Where can he get a simplified explainer so he can start taking action?
Former financial planner Joe Saul-Sehy and I tackle these three questions in today’s episode.
Enjoy!
By Paula Pant
The Bureau of Labor Statistics issues massive job revisions on Friday morning. The revisions wipe out nearly 90% of previously reported gains for May and June. This raises fundamental questions about how our most trusted economic data gets calculated.
In this episode, we break down how the system works. We examine why the revisions are so large. We explore what this means for understanding the real economy.
Friday arrives. The BLS delivers what appears routine: 73,000 new positions added in July. But the revisions tell a different story. May’s initially reported 144,000 job gains become 19,000. June’s seemingly solid 147,000 drops to just 14,000. These represent 87-90% overestimates. They fundamentally alter the economic picture for those months.
The BLS surveys 560,000 businesses each month. They use payroll data from the 12th of the month. But only 60-73% of those businesses respond by the initial release deadline. The remaining portion gets filled through statistical modeling. The models rely on historical patterns.
This approach typically produces revisions in the 20,000-50,000 range. But throughout 2025, average monthly revisions reach 66,000. That’s triple the normal size. The statistical models aren’t capturing current economic conditions effectively.
The problem becomes clear when economic conditions shift rapidly. Historical patterns become unreliable guides. The 2024 annual revision was the largest since 2009. What happened in 2009? The Great Recession. Another period when traditional forecasting tools struggled with rapid change.
ADP is a private payroll processor. They serve 460,000 companies. They provide useful comparison data. For May, their 37,000 private-sector job estimate aligns reasonably well with BLS’s revised 19,000 total. For June, ADP reports a 33,000 job loss. BLS shows a 14,000 gain.
ADP’s independent data helps validate the revised numbers while highlighting the magnitude of the initial errors.
These numbers drive real decisions. Federal Reserve officials use employment data for interest rate policy. Investors allocate capital based on these reports. Workers make career decisions based on perceived labor market strength.
When the initial data misses by 90%, everyone operates with fundamentally flawed information.
The revisions expose how fragile our economic measurement systems become when conditions change faster than models can adapt.
By Paula Pant
Interesting observations about the current housing market, meme stocks (again), GDP, Fed Meeting, Stock Market, and the latest Jobs Report updates.
Timestamps:
Note: Timestamps will vary on individual listening devices based on dynamic advertising run times. The provided timestamps are approximate and may be several minutes off due to changing ad lengths.
00:00 Introduction to Economic Turmoil
01:21 Jobs Report According to the BLS
09:23 Impact of Tariff Negotiations
12:36 The Broader Trade Landscape
16:04 Stock Market Reactions
24:11 GDP and Inflation Insights
31:52 The Fed’s Steady Hand (Interest Rates)
39:55 Housing Market Dynamics
39:40 Affordability Crisis in Real Estate
50:23 The Return of Meme Stocks
By Paula Pant
Here’s the thing about personal finance advice: what works when you have $10,000 won’t work when you have $1 million.
Yet most financial guidance treats everyone the same, whether you’re scraping together a $1,000 emergency fund or deciding whether to upgrade to business class.
Nick Maggiulli, author of “The Wealth Ladder,” joins us to break down how money strategies must evolve as your net worth grows. He’s mapped out 6 distinct wealth levels, each requiring different approaches to spending, saving and investing.
The levels start simple.
Level 1 covers anyone with less than $10,000 in net worth — that’s 20 percent of American households. Here, bad luck gets amplified. A flat tire that costs $200 could spiral into job loss and debt if you can’t afford the repair.
Level 2 spans $10,000 to $100,000 in net worth. Maggiulli calls this “grocery freedom” — you can splurge on the nicer eggs without checking your bank balance.
Level 3, from $100,000 to $1 million, brings “restaurant freedom.”
Level 4, the $1 million to $10 million range, unlocks “travel freedom.”
Getting beyond Level 4 — into the $10 million-plus territory — requires business ownership or extreme patience. Maggiulli calculates that even saving $100,000 annually after hitting $1 million takes 23 years to reach $10 million, assuming 5 percent annual returns.
The data shows income matters more than frugality, especially in the early levels. The median household income in Level 1 is $32,000, but in Level 4 it’s $197,000, and in Level 6 it reaches $4.3 million.
We discuss why homeownership dominates wealth in Levels 2 and 3, how investment assets become crucial in higher levels, and why many people in Level 4 choose “Coast FIRE” over the grinding path to Level 5.
By Paula Pant
You follow all the right personal finance advice. You know you should save more, invest regularly, and build an emergency fund.
So why does it feel so much harder for some people than others?
The answer lies in your personality.
Dr. Sandra Matz, a professor at Columbia Business School, studies the intersection of psychology and money management. She joins us to explain why one-size-fits-all financial advice often fails.
Her research found that agreeable people — those who are caring, empathetic, and put others first — have a harder time saving money.
The solution isn’t better budgeting apps or stricter rules. It’s reframing financial goals to match your personality type.
For example, agreeable people save more effectively when they view their emergency fund as protection for loved ones or a way to help others during tough times.
By contrast, competitive personalities respond better to framing savings as getting ahead in life.
This personalized approach extends beyond personality assessments. Algorithms can now predict your financial behavior using digital footprints — social media activity, spending patterns, even smartphone usage. With just 300 Facebook likes, artificial intelligence understands your money habits better than your spouse does.
The conversation also covers the darker implications. Companies exploit these same psychological insights to manipulate spending decisions. Dr. Matz discusses data cooperatives as a solution — member-owned entities where people collectively benefit from their shared information.
We dive into negotiation strategies for salary increases, breaking out of financial echo chambers, and using AI to optimize your money management without losing your decision-making autonomy.
