"We Hold These Truths to be Self-Evident"

For some people, human failures are the main story; the redemption and the progress somehow irrelevant. 

When the U.S. Declaration of Independence said "we hold these truths to be self-evident, that all men are created equal," critics will always mention that the phrase did not clearly specify women, slaves or perhaps even non-property-owning people. 

But human progress is a journey. We eventually get it right. 

At least 360,000 Union soldiers died to extend the self-evident truth of equality to all former slaves. At least 275,000 Union soldiers were wounded. At least 30,000 suffered combat amputations of limbs. 

Beyond that, progress towards equality of many types continued:
* 13th Amendment (1865): Abolished slavery and involuntary servitude.
* 14th Amendment (1868): Established citizenship and guaranteed "equal protection of the laws."
* 15th Amendment (1870): Prohibited disenfranchisement based on race, color, or previous servitude.
* 19th Amendment (1920): Extended the right to vote to women.
* 24th Amendment (1964): Eliminated poll taxes that blocked low-income citizens from voting.
* 26th Amendment (1971): Lowered the voting age from 21 to 18 during the Vietnam War
* Civil Rights Act of 1964: Outlawed discrimination based on race, color, religion, sex, or national origin in public accommodations and employment.
* Voting Rights Act of 1965: Banned discriminatory voting practices like literacy tests.
* Fair Housing Act of 1968: Prohibited discrimination in housing rentals, sales, and financing. [1, 2, 3, 4, 5]
* Rehabilitation Act (1973) & ADA (1990): Mandated equal access and prohibited discrimination against individuals with disabilities
* Title IX (1972): Guaranteed equal educational and athletic opportunities regardless of sex
* Marriage Equality (2015): The Supreme Court ruled in Obergefell v. Hodges that the 14th Amendment guarantees same-sex couples the right to marry.

The point is that the initial promise is the main thing. Our failures to be completely inclusive are secondary. We eventually get things right. 

The U.S. Declaration of Independence has been a cornerstone of global human rights, serving as a foundational blueprint for self-government. 

By asserting universal equality and the right to resist tyranny, it inspired over 100 countries to draft similar declarations and directly fueled subsequent uprisings like the French Revolution.

Its emphasis on "life, liberty, and the pursuit of happiness" provided a universal rallying cry for liberation and democratic movements worldwide. It directly influenced foundational European texts like France's Declaration of the Rights of Man and of the Citizen.

It was the first time a group of colonies successfully used the language of independence to announce their statehood and assert an "equal Station among the Nations". This provided a flexible framework for colonies in the 19th and 20th centuries to break free from imperial rule.

By establishing that governments derive their power from the people rather than divine right, the document helped to upend prevailing orthodoxies and accelerated the global transition toward modern democracy.

The document’s inherent promise of universal equality has been continuously leveraged by marginalized groups globally to demand civil rights, suffrage, and abolition.

Happy Fourth of July. It is a human achievement and an event of global impact worth celebrating. 

How Big is SpaceX Addressable Market, Really?

Nobody yet knows the eventual returns from hyperscaler high-performance computing investments, but SpaceX has estimated the artificial intelligence total addressable market at $26.5 trillion. 

If that seems questionable, Morgan Stanley projects a $25 trillion market for AI-powered robots alone by 2050. 

But such estimates always are contentious, partly because they rely on decades of growth, and the inclusion of many categories of revenue that might also be placed elsewhere. 

Consider the range of estimates for the current value of the “internet” ecosystem, which has had nearly three decades to develop. 

One of the challenges in estimating the "internet economy" is that there is no universally accepted definition. 

Depending on what is included, estimates range from roughly $7 trillion (counting only direct digital-industry revenues) to well over $40 trillion (counting all commerce conducted over internet-enabled channels).

Internet ecosystem segment	Estimated 2026 annual revenue (US$ trillions)	Value Chain Segments	Sources
Global IT spending	6.3	Hardware, software, IT services, communications supporting digital infrastructure	Gartner (Gartner)
Global telecommunications services	1.3–1.4	Fixed and mobile connectivity; largely overlaps Gartner communications services	Gartner (Gartner)
Public cloud infrastructure (IaaS/PaaS)	0.4–0.5	AWS, Azure, Google Cloud and other providers	Gartner forecast and industry estimates (Gartner)
SaaS / enterprise software	1.4	Includes enterprise application and infrastructure software	Gartner (Gartner)
Digital advertising	0.7–0.8	Search, social, retail media, video, display	(Digital Applied)
Consumer internet subscriptions	0.2–0.3	Streaming, gaming, digital media, subscriptions	Industry estimates
E-commerce platform revenues (fees, commissions—not merchandise value)	0.3–0.5	Amazon Marketplace, Shopify ecosystem, eBay, Alibaba, etc.	Industry estimates
Digital payments revenues	0.2–0.3	Payment processing and fintech platforms	Industry estimates

Adding these components (while avoiding obvious double counting where possible) suggests a reasonable market of perhaps $7 trillion to $11 trillion. 

Definition	Estimated 2026 revenue
Narrow definition (digital infrastructure, software, cloud, advertising, platforms)	$7–9 trillion
Broader definition (including telecom and digital media)	$9–11 trillion

That is still big, but nowhere near the $26 trillion figure. It might be more correct to say that, eventually, AI might be essential for supporting a wide range of economic activities that do range up into double-digit trillions of dollars.

So the SpaceX TAM is to be discounted by perhaps an order of magnitude. 

All we can measure, in the near term, is the capital investment and a relatively small, but fast-growing set of revenue streams. 

Segment	Revenue / spending level	Growth rate
Global corporate AI investment	$581.69B in 2025	+129.9% YoY linkedin
Global private AI investment	$344.66B in 2025	+127.5% YoY linkedin
Generative AI private investment	$170.87B in 2025	>200% YoY linkedin
AI infrastructure, models, research, governance funding	$143.22B in 2025	Steepest growth among focus areas; exact YoY not stated linkedin
AI software market, worldwide	~$251B in 2027	31.4% CAGR from 2022 to 2027 businesswire
AI platforms	Noted as one of the largest AI software categories	35.8% CAGR, 2023–2027 businesswire
AI applications	Roughly one-third of AI software revenue in 2023	21.1% CAGR, 2023–2027 businesswire
AI systems infrastructure software	Smaller category in 2023	32.6% CAGR, 2023–2027 businesswire
AI application development and deployment software	Smaller category in 2023	38.7% CAGR, 2023–2027 businesswire
Generative AI platforms and applications	$55.7B forecast for 2027	Forecast only; growth rate not stated in source businesswire
OpenAI annualized revenue	$25B by early 2026	Fastest-style scale-up; source describes exponential growth, not a formal CAGR linkedin
Anthropic annualized revenue	$19B by early 2026	Fast growth; source describes rapid rise, not a formal CAGR linkedin
xAI annualized revenue	$428M by early 2026	Fast growth; source describes rapid rise, not a formal CAGR linkedin
Mistral AI annualized revenue	$400M by early 2026	Fast growth; source describes rapid rise, not a formal CAGR linkedin

But near-term capex will still dwarf revenues. 

source: Futurum Group

 

source: Goldman Sachs

Balancing Human Values and AI When Concentrated Market Leadership Will Happen

In principle, it is hard to disagree with Pope Leo XIV, who argues in Magnifica Humanitas that humans values and artificial intelligence must be balanced.

Some critics will complain about AI ownership concentration and outsized market power, to protect human values.

But markets generally develop with a few leaders, whether we like it or not. 

So we still are left with the thorny task of figuring out how to do all that balancing.

Consider similar concerns about the internet. In the late 1980s and early 1990s, many academics, researchers, and early users believed the internet should remain a non-commercial, collaborative environment.

After all, the early internet was a subsidized, academic network encouraging sharing and open exchange. 

The early internet (ARPANET, NSFNET, and connected university networks) was funded almost entirely by governments and research institutions.

This culture produced enduring norms:

• open protocols

• open publication

• free exchange of software

• collaborative development.

• 
  

The turning point came after restrictions on commercial traffic over the NSFNET were lifted in the early 1990s:

• private Internet Service Providers appeared

• domain registration expanded

• browsers made the web accessible

• online retail became feasible

• venture capital entered the industry.

So some worried about:

• commercialization overwhelming academic culture

• advertising degrading user experience

• unequal access

• concentration of economic power.

Concerns about concentration of power will resemble earlier concerns about the internet. 

But the emphasis on “free” might not happen. 

State-of-the-art AI models have substantial ongoing inference costs, so the marginal cost of serving each additional user is not close to zero. And near-zero marginal costs were the enabler for “free” internet services and apps. 

As a result, "everything should be free" is less economically sustainable for AI than it was for web content. 

On the other hand, concerns about concentration of power have already emerged. But it’s a balance. Without the prospect of profit, much less capital would have flowed into software and internet infrastructure, economists will argue.

And it is likely the rule of three will emerge in various segments of the overall AI market, as is true for capital-intensive markets. 

 

source: Mercatus

The rule of three is the idea that in many competitive industries, market structure tends to settle into a small number of dominant firms because scale, fixed costs, and network effects push markets toward concentration rather than endless fragmentation. 

That often leads to a winner takes all market structure.  

In AI, that logic can show up at multiple layers: a few chipmakers can dominate hardware, a few foundation-model providers can dominate models, a few cloud/enterprise ecosystems can dominate platforms, and a few application software vendors can dominate key use cases:

• Hardware. AI chips and the infrastructure around them are capital-intensive, with high fixed costs and strong scale advantages, which makes concentration likely.

• Models. Frontier model development also has steep training costs, data advantages, and distribution effects, so a small set of model leaders can emerge even if many models exist in the long tail.

• Platforms. Cloud and AI distribution layers can become winner-take-most because users gravitate to ecosystems with the best tooling, trust, integrations, and developer gravity.

• Software. Application layers are often more fragmented than infrastructure, but in categories with strong workflow lock-in or standards, the same top-three pattern can appear.

 

Not all Industries feature the rule of three pattern. That can occur when:

•  they have low fixed costs

• weak scale economies

• highly local demand

• strong differentiation. 

Examples include many local services, artisanal goods, custom professional services, and some labor-intensive niches where geography and relationships matter more than national scale. Sectors with rapid product churn and low switching costs can also resist stable three-firm dominance because new entrants can displace incumbents quickly.

It’s hard to see how the various parts of the AI market can avoid developing along a rule of three pattern. 

And that means some critics will be severely disappointed.