Ai Hype Vs. Signal: What Super Bowl Lx Ads Mean For Homebuilders

For us, the Super Bowl LX sideshow wasn’t the score or the halftime spectacle.

It was 63 commercial messages, over 51 minutes of programming, at $8–$10 million per 30-second unit, quietly setting the stage for a master class in how capital behaves when technology narratives outrun operational reality.

That tension — hype versus signal — is where U.S. homebuilders now find themselves in the throes of AI’s opportunity for step-change in strategic, workflow, and customer-focused improvement.

A short post-game LinkedIn exchange captured the moment perfectly. M/I Homes Chief Marketing Officer Will Duderstadt, reflecting on past Super Bowls, reminded us that dot-coms once bought these ads. Then crypto. Now AI. Same playbook. Same bravado.

Same implicit promise: this changes everything.

Same reality-checked track record of extraordinarily high rates of business failure.

Cecilian Partners’ co-founder and CEO John Cecilian Jr. nudged the conversation forward in a way that could help homebuilders. Super Bowl ads, he argued, are not proof of maturity. They signal capital behavior. When execution lags, marketing volume spikes. When execution works, it usually speaks more quietly.

Cecilian’s lens on the phenomenon carries a message critical to homebuilding and residential development business strategy leaders and chief operators. It’s this: dismissing AI as “noise” is just as dangerous as swallowing the hype whole.

As he put it in one of our recent conversations:

“First and foremost, when you think about AI readiness, consider how it can immediately impact your business and make your team better at their jobs. It’s not about replacement. It’s about building net-new efficiencies.”

[Press the “play” arrow to catch our video conversation here.]

The builders who will stand out won’t be the ones who bought the flashiest tool. They’ll be the ones who build a repeatable ability to learn, test, and improve how people do the work.

Builders’ reflex: cyclical thinking in a structural moment

Homebuilding organizations are exceptionally good at adapting to cycles. Rates and prices rise. Incentives increase. Pace adjusts. Land slows. Sales come fewer and farther between, then reach a trough.

Homebuilding’s parabolic ups and downs are a foundational business pattern, varying in severity and duration yet maintaining the sequence: up, peak, down, bottom. 

Over and over, the market turns, and the machine ramps again. Adaptability and resilience among homebuilders manifest within that cyclical pattern. That muscle memory has kept many companies alive for decades.

But AI readiness isn’t a cyclical problem. It’s a structural one, an Achilles Heel for long-established, cycle-tested homebuilding organizations.

What Cecilian keeps coming back to — whether in public posts or in deeper conversations — is not models, agents, or automation. It’s something far more fundamental:

Adaptability. The ability to learn—and to keep learning—as consumer behavior, expectations, and decision pathways change beneath you.

Many builders still evaluate AI through a binary lens: Is it real, or is it hype? Should we buy now, or wait? That framing bias misses the point entirely.

The more important question is: Are we capable — culturally, operationally, and data-wise — of learning faster than our customers’ behavior is changing?

AI doesn’t fix broken systems. It exposes them.

One of Cecilian’s sharpest observations cuts through the noise:

When data is fragmented, inconsistent, or siloed, AI doesn’t resolve uncertainty — it amplifies it.

That’s why the most dangerous assumption right now is that a new software layer will resolve foundational messiness. Cecilian didn’t mince words:

“You never want to adopt software and assume it’s going to fix your problems. The dirty secret is that it will create more problems.”

In homebuilding, that shows up immediately when fragmented data and inconsistent definitions collide with “AI-powered” promises.

• Closings that can’t be clearly tied to a specific lot, plan, price, contract date, or entity
• CRM systems that don’t integrate cleanly with finance or operations
• Marketing dashboards that optimize clicks while sales teams struggle to qualify intent.
• Customer journeys are treated as linear, even though they are anything but

AI doesn’t magically repair those fractures. It simply accelerates their consequences. The tool doesn’t heal the system. It stress-tests it. That’s why the question isn’t “Can AI do it?” It’s “Can we run it reliably in our environment?”

Reliability requires:

• Structured data
• Clear workflows
• Human accountability
• Leadership patience

Those are not technology problems. They’re organizational learning, full stop.

The quiet winners won’t look like Super Bowl ads

This is where the Super Bowl metaphor becomes useful — if you read it correctly.

The eventual winners of each tech wave rarely resemble the loudest advertisers. They resemble organizations that:

• Start with small, human-led use cases
• Pilot before scaling
• Work backward from outcomes instead of forward from tools
• Learn in-market, not in slide decks
• Empower frontline teams — the people closest to customers — to test, fail, adjust, and learn again.

Cecilian consistently points to examples where leadership treated digital transformation not as a bolt-on but as a belief system. The practical path forward isn’t performative. It’s a learning discipline — outcome first, then reverse-engineering the workflow, data, and accountability. Cecilian pares this down this way.

“I typically think about things in an outcome-focused way, then work backward… you don’t try to find a solution; you identify the problem first.”

That logic naturally points to piloting, not proclamations — and to doing the work where buyer truth lives.

And that’s why his advice on whocarries change is so front-line centered:

“Those people who carry it forward operationally are the ones who are really boots on the ground… builder sales reps… a VP or SVP of sales… maybe even a division president.” In other words: if your AI initiative can’t be tested, validated, and improved by the people closest to buyers and to the friction, it’s not readiness — it’s theater.

Builders like Lennar and Taylor Morrison haven’t waited for perfect clarity. They committed to learning faster than the market. Not because AI was fashionable — but because customer expectations were changing, whether builders liked it or not.

Consumers haven’t stopped valuing homes. They’ve changed how they decide.

This is the part many builders underestimate. Despite affordability pressures, insurance volatility, rate anxiety, and qualification friction, consumers still want a home of their own. That truth hasn’t changed.

What has changed? How households lever trust, authenticity, and relevance as meaningful markers in their journey to a purchase.

Today’s buyers – across age cohorts – arrive informed. They know what they want. They know what hurts. And they are hypersensitive to friction that feels unnecessary or tone-deaf.

The opportunity for AI isn’t replacing people. It’s helping people listen better, respond faster, personalize intelligently, and meet buyers where they actually are, not where the process assumes they should be. This reframes what “AI in homebuilding” even means. If the conversation stays trapped at chatbots and hype, builders miss the real leverage points in marketing and customer engagement.

Cecilian’s non-nuanced take is plain and practical:

“AI chatbots are great, but you can do so much more with personalization, lead scoring, different offerings, and virtual design centers.” That’s not sci-fi. That’s simply meeting buyers where they already are — and doing it with more relevance, speed, and consistency.

But that only works when organizations are willing to learn:

• What buyers truly value
• Which signals matter versus which are noise
• How to adapt engagement models without breaking trust

That kind of learning cannot be outsourced to software.

The real challenge: learning how to learn

Strip away the hype, the ads, the jargon, the fear—and what remains is a leadership test. AI readiness is not about courageously buying technology. It’s about courageously confronting how your organization learns.

Can you:

• Pilot without over-committing?
• Measure outcomes instead of activity?
• Let frontline teams teach the enterprise?
• Accept short-term ambiguity in exchange for long-term capability?
• Break the habit of waiting for the cycle to turn before changing behavior?

Super Bowl LX reminded us how loud hype can get when capital is impatient.

Homebuilders don’t need louder narratives. They need quieter competence. Because the builders who win the next decade won’t be the ones who bought the biggest tools first.

They’ll be the ones who learned – deliberately, humbly, continuously – how to keep learning about the people they serve, even as the ground keeps shifting under everyone’s feet.