SpaceXSI is Not Playing a Traditional "Telecom Services Game"

Ignore for the moment the suggestion that SpaceXSI already is among the largest communication service providers globally, if one measures only total firm revenue. Starlink's revenue is in the $17 billion range, so Starlink alone would not show on this list.

But maybe that is not the point. Maybe Starlink’s ultimate value is its support of the other higher-valuation revenue elements that would come to depend on Starlink connectivity. So the game SpaceXSI is playing is not necessarily “telecom service” but compute, and connectivity is an essential underpinning of that business.



source: Economic Planning Council

Telecom subscriptions generate cash flow and help finance the constellation, but connectivity could become an enabling asset for SpaceX's ambitions in distributed computing, AI services, autonomous systems and space-based compute.

Conventional telecom model	Integrated AI infrastructure model
Sell a connection to a customer.	Connect an intelligent system to its operating environment.
Revenue depends on subscribers, pricing and data consumption.	Revenue could also depend on the value of the work performed.
Competition focuses on coverage, capacity, reliability and price.	Competition also involves integration with compute, models, devices and applications.
The network is principally an expense-producing asset that earns a return.	The network can be an enabling asset that improves the economics of several businesses.

The point is that Starlink is playing a different game than most telecom service providers. While the direct revenue is helpful, the value arguably lies in the underlying support for other lines of business that have much-higher valuation multiples.

SpaceXSI, AT&T, Verizon, T-Mobile and Beauty Contests

AT&T, Verizon and T-Mobile have been under pressure since SpaceXSI suggested it would be competing in the mobile phone business, and has followed up by acquiring 800-MHz low-band spectrum ideally suited to inside-building connectivity, an important consideration since satellite-to-phone only works when there is line-of-sight. 

According to Elon Musk, the purchase is "the last critical piece of the spectrum puzzle needed for SpaceX to provide complete phone coverage in America".

On October 9, 2026, T-Mobile US shares plunged 13 percent. the company's worst single-day drop since 2013.

AT&T shares fell approximately almost 10 percent, the worst single-day drop since 2000. Meanwhile, Verizon Communications declined about nine percent,  Verizon's worst single-day loss since July 2002.

Skeptics will argue the impact is out of proportion to the nature of the threat, as SpaceXSI still has to acquire or build a terrestrial transmission infrastructure. 

The impact on SpaceXSI was the opposite. Based on SpaceXSI's current market capitalization of $2.14 trillion, 3.5 percent of its market value is approximately $74.94 billion. So it might be argued that the 3.7-percent increase in market valuation on Oct. 9, 2026, which was about $75 billion, reflects the $8 billion investment in spectrum. 

In other words, SpaceXSI valuation increased about 10 times more than the cost of spectrum acquisition. 

On Oct. 9, SpaceXSI gained $27 billion, even using the low figure of 1.24 percent increase for the day.

The mobile service providers lost about $50 billion. 

You might argue that is an example of the “Keynesian beauty contest” at work. The Keynesian beauty contest is an economic and behavioral finance concept introduced by John Maynard Keynes in 1936 to explain how speculative markets and asset prices fluctuate.

The basic idea is that investors buy some stocks not because of strong company fundamentals, but because they believe others will buy them and drive the price higher.

The analogy is a beauty contest in which judges are rewarded for selecting the most popular choices among all judges, rather than those they may personally find the most attractive. 

As it pertains to SpaceXSI, at least some investors arguably price the company not on its current cash flows or fundamentals, but on what they believe other market participants will pay. 

It is, in other words, a narrative-driven market. We saw this during the dot-com bubble. But it has happened before then, as well. We call these periods “manias” or “bubbles.” 

I’m not arguing we are in a similar bubble at the moment. Maybe we are, maybe we will be, but it is not certain we are at the moment. 

Instead, we do see a beauty contest logic at work, where valuation is less on fundamentals and more on expectations. It might be unfair, but SpaceXSI currently benefits from the beauty contest, while the big three mobile service providers suffer.

Era and Mania	The "Prettiest Face" (The Hype Asset)	The Real Underlying Catalyst	The Beauty Contest Driver	The Collapse Trigger
1636–1637 Tulip Mania	Rare, variegated tulip bulbs (e.g., Semper Augustus).	The introduction of exotic flowers to the Dutch merchant class and early futures contract innovations.	Buyers knew a single bulb couldn't generate income. They bought purely because the broader Dutch public was highly coordinated in bidding up bulb futures, ensuring a "greater fool" would buy it next.	In February 1637, buyers at a routine auction in Haarlem simply failed to show up, breaking the psychological chain of assumed demand.
1720 South Sea Bubble	Shares of the South Sea Company.	A British government debt-for-equity swap scheme wrapped in a theoretical monopoly over South American trade routes.	Investors recognized that actual trade was non-existent. However, because the King and high-ranking politicians were buying and promoting it, citizens bought shares assuming the royal endorsement would force the public to keep bidding it up.	The company's own insiders realized the stock was dramatically overvalued and quietly began liquidating their positions, sparking a mass panic.
1840s Railway Mania	Shares of newly proposed railway companies.	The industrial revolution and the British government relaxing rules on corporate formation, combined with cheap credit.	While railways were revolutionary, the market funded thousands of overlapping, impossible routes. Investors didn't look at track viability; they chased the "railway" tag because newspaper columns constantly validated it.	The Bank of England raised interest rates to combat inflation, draining the cheap credit that speculators relied on to buy shares on leverage.
1920s The Roaring Twenties	Industrial conglomerates, radio technology stocks, and investment trusts.	Widespread adoption of electricity, automobiles, mass media, and the creation of retail margin accounts.	Speculators freely admitted stocks were expensive. They bought because loose margin requirements (borrowing up to 90% of the stock price) meant a massive wave of new retail money was constantly entering the market.	Corporate earnings began cooling in mid-1929, leading to minor margin calls that snowballed into the catastrophic Black Thursday crash of October 1929.
1997–2001 Dot-com Bubble	Any company with a ".com" suffix or internet business plan.	The commercial birth of the World Wide Web and massive telecom network buildouts.	Traditional valuation metrics (P/E ratios) were abandoned. Investors bought companies with zero revenue because they knew institutional mutual funds were suffering from intense FOMO and felt forced to chase internet listings.	Major tech backbones (like Cisco and Intel) reported spending slowdowns, revealing that startup capital was burning out, which ended the assumption of infinite demand.
2024–Present AI Infrastructure Boom	Hardware providers (GPUs), liquid cooling firms, and next-generation data center networks.	The emergence of highly scalable Large Language Models (LLMs) requiring massive parallel computing power.	High-frequency trading algorithms and retail momentum traders target companies based on the size of their "AI capital expenditures." Traders buy not for the immediate dividend yield, but because tech giants are locked in an arms race and must keep buying hardware.	Highly variable; heavily tied to micro-catalysts, structural demand shifts (such as ultra-efficient compute open-source models), or data center budget fatigue.

U.S. Court Rejects Structural Remedies for Google AdX: Perhaps Not a Surprise

The U.S. District Court in Virginia has rejected the U.S. Department of Justice structural remedies in the case of Google advertising antitrust, and instead ordered behavioral remedies. 

The DoJ had asked for divestiture of AdX, among other remedies. Some would have questioned whether divestiture and untangling was feasible, in any case.  

A forced divestiture would likely have meant:

• Possible loss of about 4.1 percent of Google's revenue and 1.5 percent of operating profit (2020 estimate)

• Loss of vertical integration (ability to run the ad server Google Ad Manager, the exchange (AdX) and the buy-side tools all in one stack)

• Losing AdX privileged access to ad server auction data and demand

• Losing capabilities such as  "last look" that advantaged AdX bids over rival exchanges

• Losing the ability to steer publisher and advertiser demand toward its own exchange by default

• Losing a business moat compared to Xandr (Microsoft), PubMatic, Magnite or OpenX.

The financial hit from losing AdX's direct revenue arguably would have been modest. The larger implications were competitive: 

• Losing the ability to internally route demand and auction advantages toward its own exchange

• Losing market share to rivals in the near term

• Facing execution risk from a messy technical separation.

Divestiture would not have affected Google's dominant position in the broader digital ad market (search, YouTube, Google Ads), as none of those were alleged to be monopolies. 

The actual behavioral remedies will be agreed upon by Alphabet and DoJ over the next month. 

Court watchers might have bet on behavioral rather than structural remedies. 

In modern U.S. computing history, courts and agencies overwhelmingly settle on behavioral remedies even after finding liability, and the handful of times a true structural breakup was ordered, it either got overturned on appeal or never survived to implementation. 

The one clean exception is AT&T in 1982 (not a "computing" company, but the antecedent case for how computing cases are usually discussed).

Case	Period	Allegation	Remedy Sought	Outcome	Type
United States v. AT&T (1956 consent decree)	1949–1956	Monopolizing telecom equipment	DOJ sought breakup	Settled: AT&T confined to regulated telephone business, barred from computing/commercial ventures	Behavioral
United States v. IBM	1969–1982	Monopolizing mainframe computing	DOJ sought full breakup	DOJ voluntarily dismissed the case in 1982 as "without merit"	None (dropped)
United States v. AT&T	1974–1982	Monopolizing local/long-distance telephony	DOJ sought breakup	Settled via consent decree: AT&T split into seven regional "Baby Bells"	Structural
United States v. Microsoft	1998–2001	Monopoly maintenance (browser tying)	DOJ sought company split (OS vs. applications)	District court ordered breakup (2000); reversed on appeal; settled 2001 on conduct terms	Behavioral (final)
European Commission v. Microsoft	2004	Abuse of dominance (Windows Media Player tying, interoperability)	Conduct remedies + unbundling	Fine + required unbundled Windows version and interoperability disclosures	Behavioral (with a quasi-structural unbundling element)
FTC v. Intel	2009–2010	Exclusionary dealing with OEMs	Behavioral remedies	Settled via consent order; no divestiture	Behavioral
FTC v. Qualcomm	2017–2020	Exclusionary licensing practices	Injunctive/behavioral remedies	9th Circuit reversed district court; FTC lost entirely	None (FTC lost)
EU v. Google (Shopping, Android, AdSense)	2017–2019	Self-preferencing, Android bundling, ad exclusivity	Conduct remedies + fines	Fines (~€8B combined) plus behavioral conduct changes; no breakup	Behavioral
United States v. Google (Search)	2020–2025	Illegal monopoly via default-placement deals	DOJ sought Chrome/Android divestiture	Judge Mehta (Sept. 2025) denied divestiture; ordered data-sharing and end to exclusive default contracts	Behavioral
United States v. Google (Ad Tech)	2023–2026	Illegal tying of ad server and exchange	DOJ sought AdX divestiture	Judge Brinkema (Sept. 2026) denied divestiture; ordered behavioral remedies	Behavioral
FTC v. Meta	2020–2025	Illegal monopoly via "buy or bury" acquisitions	FTC sought Instagram/WhatsApp divestiture	Judge Boasberg (Nov. 2025) ruled FTC failed to prove current monopoly power; case dismissed	None (FTC lost)

Of eleven major computing/telecom cases spanning roughly 70 years, only the 1982 AT&T case resulted in an actual, implemented structural remedy. 

Microsoft's breakup was ordered but reversed before it took effect. 

Three of the most recent, highest-profile cases (Google Search, Google Ad Tech, Meta) all had the DOJ or Federal Trade Commission explicitly request divestiture, and in every one of them the court either declined to order it or ruled the government hadn't proven its case at all.

Courts in Microsoft, Google Search, and Google Ad Tech all cited the risk of "incredibly messy and highly risky" separations of deeply integrated software/data systems. Judge Amit  Mehta used almost that exact language on Chrome, and Judge Lconic Brinkema's opinion in the AdX case echoed Google's own arguments about technical infeasibility.

Judge Mehta explicitly distinguished growth from "superior product, business acumen, or historic accident" versus growth from illegal conduct, and found Google's dominance wasn't attributable enough to the violation to justify divestiture.

In the Google ad tech case, testimony raised real doubt about whether a workable buyer even existed for AdX, since a divested asset built to be part of one company's stack often isn't viable standing alone.

Fast-moving markets are another issue. Judge James Boasberg's Meta ruling leaned on the idea that computing markets change too quickly for old monopoly findings to still describe today's competitive reality, undermining the case for any remedy, structural or not.

Why "AI" is Not Like the Internet or Dot-Com Bubble

For some of us, analogies between the internet bubble around the turn of the century and a potential AI bubble often emphasize excesses of investment, but also questionable accounting practices and, in a few notable cases, outright fraud (Enron, Worldcom). 

If the basic AI market danger can be stated as overvaluation leading to overinvestment, creating financial stress that encourages aggressive accounting that then slips over to illegal actions, the main danger right now is still overinvestment. 

But accounting assumptions seem to raise some issues.

At least some observers of the high-performance computing industry and neocloud providers worry about possible financial excesses such as off-balance-sheet financing of graphics processing units; infrastructure overinvestment; circular financing and GPU depreciation assumptions. 

The legitimate concern is that demand will not ultimately support the supply, leading to a bubble collapse of firms and significant financial losses for investors. 

But accounting assumptions are among the contributing issues. The concern is that what is lawful might not be wise, at scale. 

But there might be some new information on GPU useful lives that allays some of the concern. Secondary market values of Nvidia H100 GPUs, an older generation, seem to be quite strong. 

NVIDIA H100 GPU prices in 2026 suggest that the cost of refurbished units is in the mid-80-percent range of new units. 

That is a  narrower discount than buyers expect from the refurbished category, where 30- to 50-percent discounts are typical. 

A mid-80-percent floor on a three-year-old accelerator suggests demand is strong enough that even second-hand units hold most of their value.

Older GPUs remain useful for operations other than frontier model operations. Even if the highest value for the latest generation of chips is to support frontier language model training, inference operations can still use older GPUs. Beyond that, many batch operations can be completed using processors that are five to six years old. 

So depreciation schedules embodying assumptions about six-year useful life are not an accounting trick. 

There are other users of such devices and chips as well. 

Still, there is some evidence that used GPU prices for the latest generations might depreciate faster than did older generations, as new generations are released faster.  

All that matters because depreciation assumptions bear directly on reported profits. 

If a GPU's true economic life is three years but that asset is depreciated over six years, the company understates depreciation expense and also overstates net income for years one to three.

It also then will take an accelerated depreciation later, which lowers reported income. 

Secondary market values for H100s seem to provide reassuring evidence that a six-year deprecation schedule is grounded in reality, and does not distort earnings. 

Still, some might worry about Enron-style excesses, but Enron’s accounting practices were not simply unwise, but unlawful. The same might be said of Worldcom.

Still, the main problems with the dot-com bubble relate to mistaken assumptions about demand, and subsequent oversupply. 

Question	Dot-com/Enron-era warning	AI equivalent
Is demand real?	Internet traffic was real, but forecasts became extreme	AI usage is clearly real—but is ultimate willingness to pay keeping pace with compute investment?
Does revenue come from outside the ecosystem?	Telecom companies sometimes effectively sold capacity to companies whose own economics depended on the same boom	Are AI companies buying from each other in ways that make industry revenue look larger than end-user demand?
Is infrastructure earning its cost of capital?	Fiber existed, but often couldn't generate adequate returns	Are GPUs/data centers/power assets generating sufficient cash flow over their useful lives?
Are accounting profits turning into cash?	Enron's mark-to-market profits could precede cash realization	Are AI-related profits accompanied by operating cash flow?
Are assets fairly valued?	Enron used models to value difficult-to-price assets	Are assumptions about GPU useful lives, residual values, utilization and AI infrastructure returns realistic?
Where is the debt?	Enron obscured liabilities through SPEs	Are AI infrastructure obligations sitting on balance sheets or in partnerships/project-finance structures?
Who ultimately bears the risk?	Financial structures redistributed risk	Who owns the downside if AI demand disappoints—AI developers, hyperscalers, chip companies, landlords, lenders or investors?
Is growth organic?	Acquisition and financial engineering could sustain reported growth	Are customers independently generating AI revenue, or is capital circulating among AI companies?
What happens if growth slows?	Small reductions in demand could make enormous infrastructure investments uneconomic	What happens if inference demand grows 30% instead of 100%?
Does valuation require perfection?	Dot-com valuations incorporated extraordinary future growth	What assumptions about revenue, margins and AI productivity are embedded in today's valuations?

To be sure, one resonant concern is the use of special purpose vehicles to move capital investment off balance sheets. 

To be fair, other capital-intensive industries, such as airlines, have used SPVs to finance aircraft. Power utilities use them for power plants. 

But it’s an area of concern. 

Circular transactions between value chain participants also are familiar issues. When the same $100 billion can show up as a chipmaker's revenue, a lab's funding, and a cloud's backlog, actual demand can be obscured.

On the other hand, Enron and Worldcom were guilty of outright fraud. Enron's core energy-trading business was dependent on accounting assumptions and actions. 

Nvidia, Microsoft, Amazon, Alphabet, and Meta have enormous real, profitable, non-AI-dependent businesses generating current cash flow.

The hyperscalers are unlikely to be in danger of an Enron-style collapse. But some neocloud providers without the existing cash flow and profits from other lines of business are at greater risk.

And that is why depreciation assumptions matter, especially for neocloud providers. 

But again, those assumptions ultimately matter only if demand does not develop as many expect. Yes, there are timing issues. 

Ideally, revenue scales in line with investment.

But it is ultimate demand that matters most, even if gross investment levels and payback timing also matter.