At least on mobile devices, ChatGPT remains the share leader, followed by Gemini and then Claude, say analysts at Apptopia. Probably the biggest change is how much share Meta AI has gained, as it now is the fourth-largest model, in terms of mobile device use.
And it appears the model market is approaching a zero-sum game, where one model’s gains must come from another’s loss, as global usage rates continue to slow. That means a model mostly grows by taking share from another provider.
And while the market is not yet “stable,” opportunities to dramatically reshape market structure are dwindling. The rule of three appears to be shaping up.
Capital-intensive industries tend to reach a stable share pattern led by three firms. Just as significantly, the market leader will tend to have twice the share of provider two, which in turn tends to have twice the share of provider three.
And since share tends to correlate with profit margin, it really matters whether a firm is first or second in a market. Often, the market leader has four times the market share of provider number three.
By now, if a model is not in the top three, it is very unlikely to break into the top ranks, history might suggest.
It always is difficult to separate correlation from causation in any complex endeavor. Consider the impact artificial intelligence might have on employment.
Big layoffs at enterprise-sized firms, said to be driven by new AI potential, essentially shift spending from people to tokens but without clear direct financial returns.
So although we are very early in the process of adopting AI, we still know very little about actual AI impact on jobs.
A new study by Ramp and Revilio Labs that suggests artificial intelligence adoption actually increases the number of jobs at firms using AI, rather than decreasing employment.
Or does it?
The study itself suggests a possible “correlation” rather than direct causation: “Companies that adopt AI look very different from companies that never adopt,” the report notes. “AI adopters are larger, more engineering-intensive, more likely to be venture-backed, and were already growing at a faster rate before adoption.”
And that might suggest correlation: the AI adopter firms were growing faster even before AI was adopted.
It might plausibly also be the case that companies best able to make AI investments can do so because they already are growing revenues and headcount.
“Companies making the largest AI investments grow employment by roughly 10 percent on average following adoption, while low-intensity adopters see no statistically significant change,” the report states.
Again, the point is that fast-growing firms typically are those adding headcount faster.
And when the report notes that “among companies making the largest AI investments, the share of entry-level workers increased by 1.15 percentage points compared to not-yet adopters, that might also be because such firms are increasing employment virtually across the board.
That is not to say AI adoption did not aid employment growth, but only to say we cannot really prove AI was the difference maker, as the data shows the firms adding AI services or apps were faster-growing before AI was added.
That sort of thinking is in line with other studies of technology adoption that tend to show better-managed firms also are better at integrating new technology.
Study/Paper
Key Findings
Source
Bloom, Sadun & Van Reenen (2016/2017): "Management as a Technology?"
Management practices (WMS) explain ~30% of TFP gaps; treated as technology-like capital; positive interaction with IT; large cross-country/firm variation.
Better-managed firms might have strong practices in monitoring, incentives, target-setting and talent management, for example. In other words, they have intangible assets that help explain why they are better able to take advantage of new technologies.
Such firms often also have higher productivity, growth rates and profit margins, making it hard to isolate technology's independent contribution to outcomes. That might be the case with the Revelio Labs study.
Conversely, poorly-managed firms may lack the complementary skills, processes, or culture to adopt effectively, leading to slower or failed implementations, perhaps with near-term productivity dips as organizational effort is shifted to learning how to use the new tools.
Highly-publicized mass layoffs often are said to be about AI displacement, but often are mostly about correcting earlier overstaffing or simple ways of shifting budgets from people to investing in AI.
The point is that we cannot discern much, yet, about the actual impact of AI on jobs.
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
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.
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
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.
