Allbirds Throws a "Hail Mary" Pass

Allbirds, the shoe company, has sold its shoe business to American Exchange Group and wants to pivot to supplying high-performance computing to firms that are not comfortable or able to work with hyperscale suppliers. 

We might wish the firm luck with its pivot. On the other hand, it might be the clearest sign of excess in the artificial intelligence market. That isn’t to say there is froth everywhere. But there might be some similarities to Pets.com, perhaps a poster child for investor exuberance during the dot.com bubble. 

Both companies built their identities around a specific consumer promise, but were unable to reach profitability.

Pets.com sold dog food below cost and lost money on nearly every order once shipping was factored in. 

Allbirds similarly struggled with margins.

Pets.com simply ran out of cash. Allbirds was able to sell its core business. 

The pivot is the issue. 

Feature	Pets.com (1998–2000)	Allbirds/NewBird AI (2026)
Primary Trend	The "Dot-com" Bubble	The "AI/Compute" Boom
Core Product	Pet Supplies (Low margin/High weight)	GPU Capacity (High Capex/High demand)
Marketing Hook	The Sock Puppet (Brand awareness)	Sustainability (Old) / "GPUaaS" (New)
Stock Behavior	Rapid IPO growth followed by crash	Massive intraday "Pivot Pump" (800%+)
Outcome/Status	Became the face of the 2000 crash	Currently attempting a high-risk transition

Pets.com had no logistical or supply chain advantage over existing pet retailers. As did many other firms, it simply appended the “.com” suffix to its name. Allbirds seems to be doing the same using “AI.”

Allbirds has no obvious AI infrastructure expertise, proprietary technology, or data moat. The pivot is a financial and PR maneuver, not a strategy grounded in capabilities. 

Can a footwear company successfully manage the technical and cooling requirements of a tier-four data center? One might argue they can buy graphics processing units and hire personnel who do such things now. 

Pets.com proved that brand awareness doesn't solve logistics. NewBird AI now has to prove that it can surmount the "compute" expertise gap in a market segment with lots of other competitors, such as bitcoin miners similarly pivoting to high-performance computing “as a service.”

Not Even the NFL will be Immune to Supply and Demand Forces

For sports viewing and revenues, as with any other product, supply and demand do matter. 

Huge demand will tend to find a supply, no matter how much the government tries to do about it, while falling demand will lead to changes in supply. And that might already be happening. 

So it likely will be with the Federal Communications Commission’s inquiry into sports broadcasting rights. 

No matter what regulatory tweaks the FCC might pursue in its ongoing sports broadcasting inquiry, live sports rights will remain extraordinarily valuable, at least in the short term. 

source: BCG 

But long term might be quite another story. Regional sports networks already are feeling the strain as a combination of cord cutting and a shift to streaming are threatening the market.

And global sports rights, though growing, are expected to slow. 

source: Deloittte 

A few categories seem better protected, though. Between 2014 and 2024, the top 10 sports properties increased their global media rights value 113 percent, from roughly $15 billion to $32 billion, while the rights of the next 20 properties grew from about $5 billion to $7 billion, or about 40 percent.

High-demand content (NFL, the Olympics, Super Bowl, FIFA World Cup, March Madness finals) might retain a premium. Other content might see less demand and value. 

source: BCG 

The problem is the long term. Short form social media increasingly seems favored by younger viewers, displacing viewership of “full games.”

Study	Year	Key Finding	Link
YouGov Global Sports Survey (via Boston Brand Media)	2023	Only 31% of sports fans aged 18–24 watched live full-length matches, vs. 75% of fans aged 55+	bostonbrandmedia.com
Morning Consult — Gen Z & Sports	2022–23	Gen Z's overall interest in sports remains significantly below older generations; only 53% of Gen Z identify as sports fans vs. 69% of millennials; nearly half had never attended a live professional game	morningconsult.com
Vizrt Viewer Engagement Survey	2023	67% of Gen Z prefer watching sports on their phones vs. 23% of Gen X; 37% access all sports content via mobile only	vizrt.com
eMarketer / Third-Party Data (via eMarketer)	2024	Viewers over 50 are 50% likely to watch an entire game start to finish; viewers under 25 are only 30% likely to do so	emarketer.com
MediaPost / Nielsen "Total TV Dimensions 2024"	2024	Median age of linear TV sports viewer rose from 44 (1995) to 55 (2023), growing 25% vs. 15% population growth	mediapost.com
BCG — Beyond Media Rights	2026	Younger viewers watch fewer minutes of games on both broadcast and OTT; they consume sports via near-live social clips and YouTube highlights rather than full games	bcg.com
GWI Sports Viewership Trends	2025	Share of 16–24 year-old European sports fans watching highlights/recaps online weekly grew 22% since Q2 2024; only 27% of total sports fans watch full games on TV weekly	gwi.com
Deloitte — Re-imagining Media Rights	2025	Short-form sports content on YouTube grew 45% in 2024, totaling 35 billion hours; media rights growth rate slowing from 7.1% CAGR (2014–19) to ~2.7%	deloitte.com
YYZSportsMedia / NBA Data	2024	40% of Gen Z prefer watching highlights over full games; NBA traditional viewership down 19–25% year-over-year	yyzsportsmedia.com
Georgetown Law Tech Review	2024	Viewers aged 18–34 spend 60% of TV time streaming vs. 18% for viewers 65+; cable viewership dropped below 50% of total TV usage for the first time in July 2023	georgetownlawtechreview.org

Generation Z (roughly ages 10 to 28) is less likely to watch live games in full, and far more likely to consume sports through short, on-demand snippets. 

A recent global survey found that just 31 percent of sports fans aged 18 to 24 watched live full-length matches, compared to 75 percent of fans aged 55 or older:

• attention span is shorter (or consumers are accustomed to jumping between content bits)  

• digital platforms are preferred (on demand)

• Personalization (one-size-fits-all does not resonate with a generation raised on algorithmically personalized content feeds)

• Interactivity and viewing on mobile devices

• Cultural relevance (Gen Z often enjoys creator-driven content that is more irreverent, fast-paced or tailored to internet culture).

Whether the FCC has much leeway to change matters is debatable. Even if the problem is fragmentation of the viewing experience, as well as higher costs, so long as demand exists, costs will climb. 

U.S. football fans wanting to watch every National Football League game must currently spend between $935 and $1,500 annually for full NFL access across 10 services, for example. 

That speaks to the demand. And that means distributors will continue to drive up the underlying costs of such premium content that ultimately get passed along to consumers, at least in the short term. 

The FCC’s inquiry) is narrowly focused on fragmentation, consumer access to free over-the-air broadcasts, and whether current rights deals hinder local stations’ public-interest obligations. 

It does not grant the agency authority to cap rights fees, rewrite league contracts, or dictate how much distributors (broadcast, cable, or streaming) are willing to pay. 

The FCC cannot “fix this” because sports rights are determined in a competitive open market where leagues auction scarce live inventory to the highest bidders.

Live professional and major college sports have structural advantages that set them apart from almost any other programming:

• scarcity and live appeal that make them one of the last reliable “water-cooler” events in a fragmented media landscape

• premium demographics (affluent, hard-to-reach male viewers)

• monopoly-like supply (leagues control their own content and can sell rights collectively.

Much public policy chatter is about theater and perception, so we should not be surprised when government officials say they want to “do something” about a problem. 

But there is a “problem” sports team owners do face. 

Every time a league sells a new exclusive window to a new platform, two things happen:

• total rights revenue goes up

• the number of fans who can actually watch goes down. 

Up to this point, that tradeoff was tolerable because the revenue gains far outweighed the audience losses. But a problem remains: advertising revenues are built on audiences. 

So, eventually, subscription and rights revenue gains are possibly balanced by losses of advertising revenue as audiences fragment. 

If the assumption is that fans would follow the product wherever it went, paying whatever they had to, that theory now begins to be tested. FCC rules will not affect that new test of supply and demand. 

Rights fees are not absorbed by networks; they are recovered through the consumer’s wallet in one form or another:

• streaming and cable bundles

• broadcast networks (advertising costs are passed along to consumers in product prices)

• subscriptions or ad costs are still paid for by consumers. 

The problem perhaps is not “older viewers” for whom watching their favorite team play is not discretionary. It is the younger viewers who never developed the habit who are the problem. 

At what point might disinterest finally begin to prick the balloon of rights payments? If younger viewers are not interested in watching live sports, what happens to the business model?

Robotaxi Demand: Saving Time Versus Saving Money?

One traditional way of valuing a particular technology is to create proxies for “time saved,” using typical wage rates per hour. Suppliers often must do so, even if buyers historically are skeptical of the methodology. 

source: Ark Invest 

The average U.S. adult spends nearly an hour per day driving, so the imputed labor cost of all that manual piloting runs in excess of $4 trillion per year, according to Ark Invest analyst Brett Winton. “In addition we pay $1.6 trillion annually for the actual service of driving point to point.”

So one way of modeling market size of robotaxis is to estimate time savings, then impute some sort of hourly value to that time. Perhaps key to that analysis is the assumption that the highest income earners will value their time (and tradeoffs between time and money) more than lower wage earners. 

source: Ark Invest 

Some will instinctively prefer methodologies that simply compare the cost of a product versus the revenue generated by using that product. 

source: Ark Invest

Of course, when a consumer decides to take a robotaxi they are not just trading time for money, they are also avoiding the cost of running their own vehicle. So some will model additional value there. 

source: Ark Invest

So one possible impact is fewer vehicle purchases. 

Also, it is possible that as much as 40 percent of the addressable opportunity (gross profit) is captured in the first 10 percent of metropolitan areas are commercialized. 

source: Ark Invest

Using any set of assumptions, though, among the most important would seem to be the concentration of desirable markets, which are the largest cities. 

The robotaxi market is seemingly one of those cases where the upside is enormous, but the path to capturing it is hard.

Category	Opportunities (Upside)	Challenges (Downside)
Market Size & Growth	Explosive growth potential: projected to scale from ~$0.6B (2025) to >$100B+ by early 2030s (Grand View Research)	Forecasts may be overly optimistic; profitability timelines uncertain and may take years (Business Insider)
Cost Structure	Elimination of human drivers → major long-term cost advantage vs. Uber/Lyft model	Very high upfront costs: ~$150K per vehicle; ~$8+/mile in early deployments (Business Insider)
Unit Economics (Long Term)	High utilization (24/7 vehicles) could drive strong margins once scaled	Current utilization constrained by regulation, geography, and demand density
Demand Trends	Shift to Mobility-as-a-Service (MaaS); declining car ownership in cities (Grand View Research)	Demand sensitive to price and trust; adoption depends heavily on perceived safety (arXiv)
Technology Advantage	Rapid AI, sensor, and compute improvements increasing safety and capability (Grand View Research)	Edge cases (weather, pedestrians, rare events) remain unsolved at scale
Electrification Synergy	EV robotaxis reduce fuel + maintenance costs, improving operating margins (Grand View Research)	Charging infrastructure, battery degradation, and downtime management are operational constraints
Scalability	Platform economics (like Uber) + autonomy could create winner-take-most markets	Scaling is slow and city-by-city due to regulation and mapping requirements
Regulatory Environment	Increasing government support, subsidies, and pilot programs (Grand View Research)	Regulatory fragmentation; approvals required per city; sudden shutdown risks after incidents (The Verge)
Business Models	Multiple revenue models: ride-hailing, B2B shuttles, logistics, partnerships	Unclear dominant model; ride-hailing margins historically thin
Partnership Ecosystems	Strong partnerships emerging (e.g., Uber + Nvidia + OEMs) (Reuters)	Complex value chain: tech + OEM + platform + city → coordination risk
Capital & Funding	Large capital inflows (billions raised) signal investor belief in long-term viability	Cash burn is extreme; many players have exited after losses (GM Cruise, Ford Argo) (Business Insider)
Competitive Dynamics	Market likely supports multiple players (platform + tech + fleet specialization) (Business Insider)	Intense competition from Big Tech, automakers, and startups → margin pressure
Urban Infrastructure Fit	Ideal for dense cities with congestion, parking scarcity, and high demand (Grand View Research)	Limited viability in low-density or suburban/rural areas without subsidies
Safety & Insurance	Potential long-term reduction in accidents vs. human drivers	Liability risk is massive; unclear insurance frameworks
Public Perception	Early adopters show growing acceptance in pilot cities	High-profile accidents can rapidly erode trust and trigger regulation
Operational Model	Fleet optimization, routing, and pricing can be algorithmically optimized	Real-world ops (maintenance, cleaning, repositioning fleets) are complex and costly

The key challenge seemingly is cost per mile compared to the use of human drivers for ride hailing. 

If robotaxis beat human-driven ride-hailing on cost, then adoption could be highly significant. If not, niche use cases will rule. 

And technology alone is not determinative. This is a huge physical infrastructure, regulatory, capital investment and operations challenge. Likely winners will combine:

• strong regulatory navigation

• efficient fleet operations

• smart partnership ecosystems

• disciplined capital deployment.

Layer	Margin Potential	Capital Intensity	Moat Strength	Likely Outcome
OEMs	Low (5–15%)	Very High	Low–Moderate	Commoditized unless integrated
Software	Very High (30–70%)	Very High (R&D)	Very High (if winner)	Few dominant players
Fleet Operators	Moderate (10–25%)	High	Moderate	Quiet long-term winners
Platforms	High (20–40%)	Low–Moderate	Very High (network effects)	Major value capture if dominant

Still, costs matter. And some of the analysis by Ark Invest suggests that robotaxi costs will keep falling. That should help fleet operators. 

source: Ark Invest

Still, the emergence of just a few big winners on the service provider part of the value chain; the platform and software supplier parts of the business. 

But you would already have guessed that.