When Winning Is a Budget Risk: Hedging Coaching Bonuses

Prediction-market trades have turned up whose payouts nearly match college football coaches' playoff bonuses. Five line up almost exactly with Lane Kiffin's bonuses at LSU. The schools didn't place them, but third parties acting for them plausibly did.

Why would a school bet on its own success? Coaching contracts promise big bonuses for making the playoffs, advancing and winning a title. A deep playoff run can leave the athletic department owing millions it didn't budget for. A contract that pays off when the team wins covers that bill when it comes due. Like a farmer locking in a crop price, the school gives up some upside in exchange for a predictable budget.

Why go through a third party? NCAA rules forbid athletic staff from betting on games, and prediction markets count. So a school can instead pay an insurer a premium, and the insurer buys the contracts itself.

Takeaways:

• A performance bonus is a bill that only comes due when things go well.
• If people are betting on whether that success happens, you can buy a bet that pays for the bill.
• As budgets tighten and these betting markets grow, expect more of this.

Source: Josh Hendrickson, Insuring Against Success?, Economic Forces

Rewarding Judgement in AI Use

AI is changing which employee skills create value and how to incentivize these skills. EY recently announced a $100 million employee rewards program aimed at recognizing workers who develop “future-focused” capabilities and use technology to produce better outcomes. The interesting part is what EY has chosen to reward. Along with technology adoption, the firm emphasizes business acumen, judgment, adaptability, experimentation, collaboration, and innovation. As AI makes some technical capabilities more abundant, human judgment can become increasingly scarce and valuable. EY is not simply paying employees to use more AI; it is rewarding them for developing skills that make AI more productive.

That changes the incentive problem. If AI substitutes for routine research, analysis, or coding, producing more of those activities becomes a less useful measure of employee performance. Knowing which questions to ask, recognizing when an AI answer is wrong, exercising judgment when information is ambiguous, and finding new ways to combine AI with expertise can become more valuable. But these qualities are also difficult to measure. Counting hours, completed reports, or even AI usage is relatively easy; determining whether an employee exercised good judgment is not. EY's new program therefore allows employees at all levels to recognize colleagues, while other EY initiatives use simulations, coaching, and assessments of decision-making and collaboration to identify these less tangible skills.

Moving from objective output measures toward judgment, adaptability, and collaboration also changes the role of supervisors. Managers must have more discretion to decide whose contributions actually created value, making the quality and credibility of those evaluations more important. EY has argued that AI-era performance management should place greater weight on peer feedback, coaching, adaptability, and cross-functional impact, rather than relying simply on traditional manager ratings. It is also investing in more structured ways of assessing these skills: its new Career Residency program uses workplace simulations, coaching, and client situations to assess decision-making, collaboration, learning, and adaptation. As easily measured tasks become easier to automate, performance evaluation may consequently depend more on subjective judgment, but also on multiple evaluators and better ways of evaluating the evaluators.

When driving is (almost) free

While I am pet-sitting for my sister in California, she is letting me use her beach-community house and electric car. It has been nice driving past gas stations with prices so high. She also has residential solar panels to charge her car (and run her other devices). The panels generate enough power that she is almost always "off the grid" with no electricity bill. So is the cost of driving zero? Should I be driving all over the place? 

Almost. The opportunity cost is what she would do with the surplus power she produces. Her utility will buyback her surplus power at $0.05-$0.08 per kwh while her retail rate is $0.45-$0.47 per kwh. Back in Texas these would be $0.07-$0.08 per kwh and $0.13-$0.19 per kwh. The opportunity cost in California is nearly 1/10 of the price while it is close to 1/2 in Texas. In Texas, I might drive a bit more if I had an EV and solar panels. In California, I would drive a lot more.

Save Early Kids

One of the most valuable decisions in retirement saving is when to start. Over the 50 years from 1976 through 2025, the S&P 500 delivered a compound annual return of about 11.82%, including reinvested dividends. Suppose a young worker had invested just $100 each month in an S&P 500 fund and earned that historical compound return until age 65. Starting at age 25 would produce about $922,000. Waiting until 30 reduces the ending balance to about $523,000, while waiting until 35 reduces it to about $295,000. Scraping together $100 when you are 25 is difficult, but if you can do it consistently, voila you will become a millionaire.

What makes the comparison striking is how little of the difference comes from the contributions themselves. Doubling your set-aside each month will merely double the ending balance. But by starting at 25 versus 30, you contribute only $6,000 more yet finish with roughly $399,000 more. Save 15% more and finish with 76% more. Compared with starting at 35, the early saver contributes just $12,000 more but finishes with about $627,000 more. The reason is compound growth: dollars contributed early earn returns, those returns earn returns, and the process repeats for decades. Of course, future stock returns may be lower than the past 50 years, and actual returns are anything but smooth. But the lesson does not depend on the return so much. The opportunity cost of waiting to save is not primarily the contributions you miss, it is the decades of compound returns those contributions could have earned.

You can use the SEC site to do your own “what if” scenarios.

Costco Employees Can Enjoy Labor Day

 

Most major retailers remain open on Labor Day, often running special sales. Costco is unusual in that it closes its U.S. stores, called “warehouses” by the company, on Labor Day. At first glance, closing on a potentially busy shopping day looks expensive. But the visible lost revenue may substantially overstate the true cost. Former Costco CFO Richard Galanti says most of those sales simply shift to the days before or after the holiday. A shopper planning to buy groceries, paper towels, or a television at Costco may just go on Sunday or Tuesday instead. The relevant cost of closing, therefore, is not the revenue Costco could have collected on Labor Day, but the contribution margin on sales that disappear altogether rather than shift to another day.

The benefit, seemingly obvious, is even harder to quantify. Costco CEO Ron Vachris says the company closes so employees can enjoy the holidays with family and friends. That benefit may extend beyond the value of the day off itself. A company willing to sacrifice sales for its employees may generate morale, loyalty, retention, and recruiting benefits that never appear on an accounting statement. They instead show up indirectly through lower turnover, easier hiring, or greater effort. Costco therefore faces two hidden quantities: the cost of closing may be much smaller than the day's lost sales suggest, while the value to employees may be much larger than the day's wages suggest. If most sales merely shift to neighboring days while employees highly value having the holiday off, closing the stores can create value even though neither side of that calculation is readily visible.

GitLab Restructuring

 

In May 2026, GitLab announced a major reorganization that will create roughly 60 smaller R&D teams with greater autonomy and end-to-end ownership, while eliminating as many as three layers of management. This episode illustrates a basic tradeoff in organizational design. Larger functional or divisional groups make communication within a specialty relatively easy, but they can create weak incentives and costly communication across organizational boundaries. A programmer might finish her assigned code, for example, while responsibility for whether the overall product actually works belongs somewhere else. GitLab explicitly identifies layers of management and “handoffs that dilute accountability” as problems it wants to eliminate. Putting the people needed to deliver a product into a smaller team gives them greater authority, and responsibility, over the decisions affecting that product.

But organizing around small product teams creates a different communication problem. Engineers working on similar technologies may now sit in different teams, so knowledge that once flowed naturally within a large engineering group must travel across product groups. Teams can also duplicate solutions to common problems or make decisions that work for their product but impose costs elsewhere in the company. GitLab is betting that this tradeoff has changed: it plans to use AI agents to automate some reviews, approvals, and handoffs, while its current development model emphasizes small, cross-functional teams with end-to-end accountability. Organization form depends on which communication problem is more costly. Divisions facilitate coordination among specialists but weaken incentives for the final outcome; product teams strengthen ownership of outcomes but make sharing knowledge and coordinating across products more difficult. GitLab’s restructuring suggests that it now believes the first problem has become more important than the second.

Boeing / SPEEA Negotiations

 

Boeing and its roughly 17,000 engineers and technical workers represented by the Society of Professional Engineering Employees in Aerospace (SPEEA) are bargaining over new labor contracts. Both sides are taking actions to raise their disagreement values. After workers rejected Boeing’s contract offers, they also voted to authorize their negotiating team to call a strike. Boeing, meanwhile, has begun implementing a strike contingency plan designed to keep the business operating if workers walk out. Boeing says the plan is intended to allow it to continue aircraft deliveries and meet other customer commitments during a strike. Boeing also says it had hoped to spend some of those resources on employee incentives but, following the rejection, redirected the money toward strike preparations. Thus, each side is taking actions that make it easier to say “no” to the other side.

They are committing to playing hard rather than simply threatening to do so. Each side hopes these actions will be viewed as credible so as to affect the final agreement in their favor. A strike authorization strengthens the union negotiators’ ability to credibly threaten a walkout, while Boeing’s contingency investments reduce the cost to Boeing of allowing a strike to continue. Deliberately reducing your flexibility can improve the deal you receive. If your opponent knows that you have committed yourself to saying no, they may have to offer you more to get you to say yes.

Walmart Gives its FinTech a Foot in the Door

 

Fintech companies such as Chime and Current face a basic problem: acquiring customers can be expensive. They must advertise, offer promotions, or form partnerships to persuade consumers to download an app and begin using their financial products. Walmart’s majority-owned fintech, OnePay, has a very different starting point. According to Semafor, most of OnePay’s roughly 7 million customers have come from Walmart’s 1.5 million employees and approximately 150 million shoppers. OnePay CEO Omer Ismail argues that these customers are effectively acquired for free: Walmart already has the stores, website, app, checkout system, customer relationships, and traffic needed to put OnePay in front of millions of potential users.

Economy of scope can occur when an input created for one product can be shared to produce another product more cheaply. Walmart’s enormous distribution network was built to sell groceries, clothing, electronics, and other merchandise, but the same network can distribute financial services. For example, OnePay is integrated directly into Walmart’s physical and digital checkout channels, where installment loans are now powered by Klarna. A standalone fintech must build both a financial product and a way to reach customers. Walmart can use an asset it already owns to do both retailing and finance. This allows diversification to create value. Both businesses using the same underlying asset creates economies of scope across the businesses.

Accepting Late Bids?

 

Spirit Airlines’ bankruptcy has produced an unusual auction: Google agreed to pay $10 million for a trove of Spirit’s internal corporate data, including roughly 100 million emails and 500 million Microsoft Teams messages, beating a $7.5 million bid from AI firm Mercor. But after the auction closed, AI startup Micro1 offered $12.5 million, a sizable bump from Google’s bid. The bankruptcy court must now decide whether to consider the late offer. At first glance, the answer seems obvious. Spirit’s creditors want as much money as possible, so why not accept the higher bid?

Because changing the rules after an auction can affect bidding before the auction. A well-designed auction gives bidders confidence that deadlines and procedures are credible. If firms expect a losing bidder to get another chance after seeing the winning price, they have an incentive to hold back during the formal auction and wait to top the winner afterward. That can reduce competition and lower expected revenue in future auctions. Bidders on potential auctions for additional Spirit assets may adjust their bids downward. Conversely, rigidly rejecting a substantially higher late offer leaves money on the table today. There is an auction design tension because maximizing the highest observed bid is not necessarily the same as maximizing the seller’s expected revenue. Sometimes committing not to accept a better offer later can induce bidders to make better offers now.

WaPo: Social Security and Medicare going bankrupt