Compute Cash Flow: Infrastructure Tends to Lead Other EcosystemRevenue Streams

Some observers rightly note that the “time to revenue” for high-performance computing “as a service” suppliers is crucial. At the moment, for example, some would point to service supplier investments to create the infrastructure.

Some might characterize the cash flow as benefitting chip suppliers at the expense of high-performance computing suppliers, and it is hard to argue with that observation. 

But infrastructure creation first; revenues second is a classic pattern in computing. That pattern has been seen in semiconductors, telecommunications, cloud computing, internet infrastructure, and now AI infrastructure.

Yes, there always is risk, as returns are not guaranteed. 

source: Yahoo Finance 

But it is simply a fact that many computing businesses require supply-leading infrastructure. 

Firms must build capacity before customers can fully exploit it, and that investment often involves high fixed costs, before applications demand and use cases can emerge. 

Principle	Explanation
High fixed costs	Infrastructure requires enormous upfront investment before any customers are served.
Low marginal costs	Once infrastructure exists, serving additional customers becomes relatively inexpensive.
Long construction cycles	Fabs, fiber networks, and data centers often require years to build.
Induced demand	Lower prices and better performance stimulate entirely new applications.
Network effects	Infrastructure becomes more valuable as additional users and complementary services appear.
Option value	Excess capacity allows entrepreneurs to create products that previously were impossible or uneconomic.

So will there be a revenue lag for high-performance computing utilities? Yes. 

Era	Infrastructure investment	Revenue followed later through...	Time lag
Mainframes (1960s)	IBM manufacturing plants, semiconductor production, service organizations	Enterprise computing adoption	Several years
Semiconductor fabs (1970s-present)	Multi-billion-dollar fabrication plants	PC, mobile, cloud, AI chip demand	2–5 years
Personal computers (1980s)	Intel processor fabs, Microsoft software ecosystem, OEM manufacturing	Mass PC adoption	3–5 years
Internet backbone (1990s)	Fiber optic networks, routers, submarine cables	E-commerce, search, streaming	5–10 years
Mobile broadband (2000s)	3G/4G towers, spectrum, fiber backhaul	Smartphone economy, app stores	3–8 years
Hyperscale cloud (2006-present)	Massive global data centers	Cloud software, SaaS, AI services	3–10 years
Content delivery networks	Global edge server deployments	Video streaming and cloud gaming	Several years
AI infrastructure (2023-present)	GPU clusters, AI factories, power generation	AI agents, enterprise AI, robotics, scientific computing	Still developing

But that is a documented pattern:

• Chip fab investment precedes chip sales revenue

• Data center investments preceded cloud computing as a service revenues

• Railroads preceded nationwide commerce

• Electric grids preceded widespread electrification

• Fiber preceded streaming

So building GPU clusters will precede AI-native businesses.

But apparent overinvestment will always be a concern. 

Historically, such overinvestment often also occurs, whether that is long-haul fiber; dynamic random access memory, solar panels or data centers. 

The dangers of overinvestment also are real. But the necessity of infrastructure investment before applications, use cases and revenue can develop is a reality we have often seen. 

Value in Technology Value Chains Tends to Migrate to the App Layer

Slow revenue growth and lower average revenue per account are hardly new concerns for suppliers of consumer access services (mobile or fixed). 

But we should not be surprised, either. 

The rule in technology industries is that economic value tends to migrate upward in the technology stack. Network effects are one reason. But opportunities for customer relationships, loyalty and multiple revenue models also make a big difference. 

Asset	Access provider	Application
Customer relationship	Weak	Strong
User data	Limited	Extensive
Workflow integration	None	Deep
Brand loyalty	Moderate	High
Network effects	Small	Often enormous
Pricing flexibility	Low	High

So in the internet value chain, roughly half of ecosystem revenues accrue to app providers, while access providers (internet service providers, mobile service providers) get between 15 percent and 20 percent. 

Value chain layer	Typical participants	Approx. share of ecosystem revenues	Economic characteristics
User applications & digital services	Google, Meta, Microsoft, Netflix, Salesforce	45–55%	Highest margins and strongest network effects
Commerce & digital platforms	Amazon, Uber	20–25%	Transaction-based economics
Cloud & enabling services	Amazon Web Services, Microsoft Azure, Google Cloud, CDNs	10–15%	Infrastructure with higher value-added
Internet access	ISPs, cable, mobile operators	15–20%	Capital intensive, regulated, slower growth
Passive infrastructure	Towers, fiber REITs, colocation	5–10%	Stable but utility-like returns

The economic principle is simple:

• Infrastructure competes on capacity

• applications compete on customer outcomes.

Capacity usually becomes abundant, and abundance reduces pricing power.  Solutions for customer problems remain “scarce,” in the sense that customers gravitate to a relatively few apps and tend to stick with them over time. 

And scarcity supports pricing power. 

Economic force	Internet example	AI analogy
Infrastructure becomes commoditized	Broadband, fiber and mobile access become widely available	GPU clusters eventually become standardized compute utilities
User attention concentrates	Search, social media, streaming dominate consumer engagement	AI assistants and vertical AI agents become primary interfaces
Switching costs increase higher in stack	Users stay with Gmail, Office 365, Salesforce—not because of ISP	Users remain with AI workflow platforms because of memory, integrations and data
Network effects strongest near users	Facebook, YouTube, Amazon Marketplace	OpenAI ecosystem, enterprise agent platforms, developer ecosystems
Pricing power follows differentiation	ISP sells Mbps; applications sell outcomes	GPU provider sells tokens; applications sell productivity or decisions
Marginal cost falls faster below than above	Network capacity continually gets cheaper	Compute cost falls faster than value of specialized applications

In the AI ecosystem, similar value chain effects should happen. Value should accrue heavily at the app layer. 

AI layer	Future revenue share	Why
AI applications and agents	40–50%	Own workflows and customer relationships
Vertical enterprise software	20–25%	Industry-specific solutions
Foundation model providers	10–20%	Models become more competitive over time
AI cloud infrastructure	10–15%	Compute utility with economies of scale
Hardware (GPUs, networking)	5–10%	Hardware normalizes after supply shortages
Power and facilities	3–8%	Necessary but infrastructure economics

SpaceX IPO Estimates $26.5 Trillion AI Addressable Market

Not the sort of company mission one normally sees in an S-1 (initial public offering) filing!

On the other hand, the expected future revenue leans on artificial intelligence, not space or connectivity. 

So such goals as “colonizing Mars” are essentially the sizzle on the steak. 

SpaceX S1 filing 

SpaceX estimates a total addressable market of $28.5 trillion:

• $370B in "Space" (space-enabled solutions including Starship (fully reusable booster), lunar economy, point-to-point Earth transport, in-orbit manufacturing)

• $1.6T in Connectivity (Starlink Broadband ~$870B + Starlink Mobile ~$740B, plus enterprise/government)

• $26.5T in AI (compute infrastructure (terrestrial + orbital), subscriptions, advertising, enterprise applications, Grok models, consumer/enterprise/government adoption, X platform monetization. 

Year	Total Revenue	Starlink/Connectivity	Launch/Space	AI/xAI
2025 (Actual)	~$18.7B	~$11.4B	~$4.1B	~$3.2B
2026 (Est.)	~$20–28.5B	Strong growth (e.g., $18–20B+)	Stable/moderate growth	Growing but capex-heavy
Longer-term (2030+)	$100B+	Dominant	Scaled via Starship	Major contributor