Seriously, What Could They Be Thinking? Podcast

We’re back after a fun-filled summer of activities. We have so many questions!!!

On September 12, three men who agree on almost nothing (Dario Amodei, Sam Altman, and Elon Musk) all said the same thing within hours: the AI industry should slow down. Even Rahm Emanuel said he’d never seen an industry ask to be regulated. So did these guys discover a conscience, or a moat?

Jaime and Cindy do the homework before the outrage. They walk through the OpenAI incident that set this off, in which about 1,200 test agents built their own secret message board and 700 of them went on to attack Hugging Face. They explain why “the agents cheated” is really a story about the goals we set. And Jaime makes the case that “please regulate us” might have as much to do with the balance sheet as with safety.

Then they bring it back to your team, with three takeaways:

* Ask your vendors for the transparency the labs are now offering strangers.

* Your agents will hit whatever goal you set, one way or another.

* Congress has gone home, so you’re the regulator of your own shop.

Plus a four-item checklist you can start this quarter, and a mystery: one of these three men asked the world to pause AI once before, and then started an AI company. Keep your guess in your back pocket.

Keep your spreadsheets handy and your coffee strong.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit whatcouldtheybethinking.substack.com

The largest IPO in history is about to launch and Jaime and Cindy aren't strapping in quietly. SpaceX hits the Nasdaq on June 12 at a $1.75 trillion valuation, except the "space company" is now really three companies in a trench coat: rockets and Starlink, the platform formerly known as Twitter, and a cash-incinerating AI unit. The twist isn't the size; it's the quiet rewrite of the index rules that can fast-track SpaceX into the funds inside your retirement account in as little as five trading days; no seasoning, no profits required. Jaime Peters breaks down how passive investing became forced buying, why the S&P 500 alone refused to play along, and who actually pockets the upside before retail ever gets a share. Cindy Goodwin-Sak pulls the management lesson out of the chaos: when someone changes the rules underneath you, which anchors do you refuse to move? Seriously, what could they be thinking?

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit whatcouldtheybethinking.substack.com

What do you get when a $4 billion wool sneaker brand sells its entire identity for $39 million and reinvents itself as an AI GPU rental company…in 16 days? Finance expert Jaime Peters and leadership researcher Cindy Goodwin-Sak untangle the wild, woolly tale of Allbirds' transformation into NewBird AI, asking the uncomfortable question every manager should face: when a publicly traded company completely abandons its original mission, who exactly authorized that flight path? And, does it even matter if the stock surges 700%?

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit whatcouldtheybethinking.substack.com

OpenAI may be the most talked-about company in tech, but even visionary companies have to pass the budget test. In this episode, Dr. Cindy Goodwin-Sak and Dr. Jaime Peters pick up where they left off on OpenAI’s business model and go deeper, this time into the IPO mechanics that could affect everyday investors whether they realize it or not.

Jaime breaks down exactly how an IPO works: what it means to shift from private to public accountability, why fewer companies are going public today than in the 1990s, and what a trillion-dollar valuation actually represents in terms of new cash versus converted private equity shares. (Spoiler: the $40 billion and the $960 billion are not the same thing.)

Cindy brings the leadership lens with what it means organizationally when you suddenly have millions of eyes on your decisions instead of a handful of private investors, and why the forcing function of public scrutiny might actually be good for OpenAI’s long-term health.

Together they work through the uncomfortable math: rising compute costs, shrinking margins, a cash burn projected to hit $190 billion by 2030, and what all of that means for the everyday Americans who hold index funds in their 401(k)s, and who may have no choice but to own a piece of whatever comes next.

The takeaway for managers? Demand isn’t the same as health. Know your costs. Build optionality. And tell your team the truth when things are changing, even when the hype says otherwise.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit whatcouldtheybethinking.substack.com

What does it actually take to run an AI company? And can OpenAI afford it? This week, Cindy Goodwin-Sak and Jaime Peters kick off a multi-part series diving into the business of being an AI company, not just using one.

It all started with a CNBC article on OpenAI’s data center pivot that had them talking for days. So they decided to break it down for you, starting from the ground up: what goes into a data center, why AI requires specialized (and expensive) computing hardware, and just how much energy and water these facilities consume. Spoiler: think the combined electricity usage of Chicago, LA, New York, Dallas, and St. Louis, and then double it.

They also unpack OpenAI’s original strategy for scaling its infrastructure (the Stargate initiative, its partnerships with Microsoft, Oracle, Amazon, and SoftBank) and what any manager can learn from those moves around vertical integration, vendor diversification, and cost control.

But here’s the catch: OpenAI generated $20 billion in revenue last year, and their projected compute bill through 2030 is $600 billion. The math isn’t mathing. Tune in next week to find out what happens when reality sets in.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit whatcouldtheybethinking.substack.com

Twice a year, society agrees to a completely unnecessary group project.

This week on Seriously, What Could They Be Thinking?, Dr. Cindy Goodwin-Sak and Dr. Jaime Peters tackle daylight saving time: the policy that promises efficiency, delivers exhaustion, and somehow survives year after year.

They explore the history behind DST, whether it actually saves energy, what the research says about health and workplace performance, and why leaders so often leave bad systems in place long after the original rationale has faded. What starts as a conversation about the clock turns into a bigger discussion about policy inertia, decision-making, and what leaders owe the people affected by their choices.

Inside the episode:

* the wartime roots of daylight saving time

* the myth that farmers wanted it

* the shaky evidence on energy savings

* the more convincing evidence on health, safety, and productivity costs

* the debate over permanent standard time versus permanent daylight time

* practical manager takeaways on friction, policy review, and decision criteria

It is sharp, funny, and uncomfortably relatable for anyone who has ever looked at a long-standing policy and thought, this cannot possibly still be the best idea.

If the system is making people tired, annoyed, and less effective, familiarity is not a good enough excuse to keep it.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit whatcouldtheybethinking.substack.com

Employers are noticing a troubling pattern.

Graduates and young professionals increasingly ask for step-by-step instructions for everything, from routine assignments to basic workplace decisions. Tasks that once required judgment and initiative now often trigger a request for detailed guidance.

In this episode, Cindy Goodwin-Sak and Dr. Jaime Peters explore what might be driving this shift.

Drawing from recent conversations with employers, classroom experiences, and emerging research (including a widely discussed MIT study on AI and learning) they examine how a mix of pandemic disruption, over-structured learning environments, and new AI tools may be contributing to something psychologists call learned helplessness.

The result?A growing discomfort with ambiguity, reduced ownership over work, and fewer opportunities for people to develop the judgment that organizations depend on.

But the conversation doesn’t stop at the problem.

Cindy and Jaime also discuss how leaders and managers can begin to rebuild learned industriousness by changing how they delegate work and develop employees. Instead of narrating every step, they argue that great managers focus on three things:

* Clear outcomes

* Smart constraints

* Real decision authority

They also offer practical advice for professionals who want to avoid becoming stuck in “instruction-following mode” and instead build the kind of judgment and initiative that drives career growth.

If work increasingly feels like a checklist (or if you manage people who constantly ask what to do next) this conversation will challenge some assumptions about how we learn, lead, and grow. Keep your spreadsheets handy and your coffee strong!

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit whatcouldtheybethinking.substack.com

Picture this: Chicago. Christmas week. Wind doing its best to remove your face. Your kids are lobbying hard for an Uber like it’s a humanitarian need. And somewhere in the background, an algorithm is quietly asking: How much will she pay before she gives up and starts walking?

In this episode, Jaime and Cindy unpack the not-so-magic trick behind personalized pricing and dynamic pricing: why the same item can cost one person $19.99 and another person $24.99, why your airline ticket seems to “sense commitment,” and why “Richistan premium” is… not entirely a joke.

We start with a simple question from Pamela and Kelsey (shout-out to the Michael’s aisle of confusion): Why is the online price different from the in-store price for the exact same item? From there, we follow the breadcrumbs through surge pricing, cart-abandonment discounts, subscription “please don’t leave us” offers, and the not-at-all-cozy possibility of platforms optimizing not only what riders will pay, but what workers will accept.

We also go global for a minute: how the EU’s GDPR approach differs from the U.S. patchwork, why disclosure laws are starting to show up, and what “trust as an economic asset” actually means when screenshots travel faster than your PR team.

In this episode, you’ll walk away with:

* How to spot when pricing is being shaped around you (and what to do about it)

* What leaders should consider before deploying pricing strategies that rely on customers not noticing

* Why discounts feel like rewards, price hikes feel like betrayal, and how that difference can make or break loyalty

Drop your own “wait, WHAT?” pricing stories in the comments. And if you’re headed to Chicago in December, may your gloves be warm and your fares be reasonable.

Until next time: keep your spreadsheets handy and your coffee strong.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit whatcouldtheybethinking.substack.com

Nothing says holiday spirit like spending money, or getting elbowed by a grandma over a discounted air fryer.

In this episode of Seriously, What Could They Be Thinking?, Jaime Peters and Cindy Goodwin-Sak dig into why some stores looked bare before Thanksgiving while others seemed overstocked, and how leadership decisions made months ago are shaping your holiday shopping experience right now.

Our guest, Dr. Honey Zimmerman, supply chain expert and friend of the show, explains how retailers placed bets back in spring amid tariff chatter and economic uncertainty, why many cut SKUs to streamline, and what that means for prices, selection, and shipping speeds in December. We also talk about the K-shaped economy, small-business strain, and why Cyber Monday keeps eating Black Friday’s lunch.

What we cover:

* Why you saw fewer choices on shelves: the rise of SKU rationalization to manage risk and costs

* Big-box vs. small business: inventory levels diverge, with small retailers carrying roughly 20% less than last year

* The calendar reality: holiday inventory decisions are made 6–8 months in advance, often before policy and price changes are clear

* Consumer behavior shifts: sales up ~7% with fewer items per cart; more deliberate spending and easy substitutions

* Online wins again: Cyber Monday outpacing Black Friday by about 22%

* Slower ETAs: longer lead times and stockouts even for “Prime” buyers

* Data over drama: how leaders use PPI vs. CPI, transportation volumes, and NRF data to navigate uncertainty

* Omnichannel tradeoffs: why something isn’t in-store but is online (and why that’s on purpose)

* Better decisions next season: write down your assumptions so you can actually learn from what happened

Who should listen:

* Retail and supply chain leaders wrestling with demand planning and inventory bets

* Small business owners planning Q4 in a lean environment

* Shoppers curious about why prices and choices feel…weird

* Anyone who loves a good “what were they thinking?” decision breakdown

Guest

Dr. Honey Zimmerman is a supply chain practitioner-turned-professor with deep expertise in procurement, manufacturing, and logistics. She’s here to translate the data behind the holiday frenzy into clear, practical insight.

Stat snapshots we unpack:

* Black Friday: sales up about 7% year over year, but with fewer units per transaction

* Cyber Monday: roughly 22% higher sales than Black Friday

* Producer Price Index running hotter than Consumer Price Index, squeezing margins upstream

* Small business inventories down around 20% vs. last year

Bottom line:

If the shelves look sparse, it might be strategic minimalism. not a crisis. Prices feel odd because inflation’s in its abstract art phase. And yes, the supply chain folks are doing their best…please don’t yell at them.

Listen in, subscribe, and send us the head-scratching decisions you want us to unpack next. Until then, keep your spreadsheets handy and your coffee strong.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit whatcouldtheybethinking.substack.com

Is AI genuinely the next industrial revolution, or are we watching another dot-com-style J curve flatten in real time?

From NVIDIA’s outsized role in the market to why most companies still can’t point to productivity gains, Jaime Peters and Cindy Goodwin-Sak dig into the numbers, the psychology, and the leadership practices that actually move AI from pilot to payoff.

We compare today’s AI moment to two past bubbles, the dot-com era’s “Napster moment” and the 2008 housing slowdown, to explain why flattening, not falling, often breaks a boom. And we ask the tough question every CFO is already thinking: who’s going to foot the $5 trillion AI infrastructure bill if enterprise adoption plateaus?

What you’ll hear:

* The market vs. the office: Why consumer enthusiasm (hello, ChatGPT) isn’t the same as enterprise deployment

* The data reality check: Adoption stats from recent surveys and why the curve may be stalling

* The money math: $5T in expected AI spend and the annual revenue required to make it pencil out

* Productivity or pipe dream: What it would actually take for AI to deliver broad 5–10% productivity gains

* Lessons from past bubbles: How “flattening” broke the dot-com and housing booms, and what that signals for AI

* Leadership playbook: Practical steps to turn AI from novelty to value (create time to experiment, share use cases, lead by example, reward progress)

* Strategy for incumbents: Why most companies should expect evolution, not revolution, while still carving out a focused lane for real product/service disruption

Who should listen:

* Executives and investors trying to read the AI tea leaves

* Operators and team leads under pressure to “do AI” and show results

* Anyone curious about what’s signal vs. noise in the AI economy

Hot Take:

Is AI a bubble or a revolution?

Yes. It can be both, and your results will depend less on GPUs and more on leaders who know how to turn experimentation into measurable outcomes.

Listen now: Seriously, What Could They Be Thinking?, hosted by Jaime Peters and Cindy Goodwin-Sak. Keep your spreadsheets handy and your coffee strong.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit whatcouldtheybethinking.substack.com