Regulation is a Proven Way of Protecting Industry Incumbents

Are industry leader calls for regulation of artificial intelligence models a tacit form of market leader moat building, as has proven to be the case for other heavily-regulated industries?

The basic economic mechanism is well established: regulation creates fixed costs, and fixed costs favor firms with scale. Regulation also limits and shapes key contestant options in ways that can protect them from excessive and violent spending or price competition.

The analogy might be rate regulation in any capital-intensive industry such as passenger airlines, electricity or natural gas supply or telecommunications.  

In Lewis Carroll’s Through the Looking Glass, the Red Queen tells Alice: “Now here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run twice as fast as that!”

And that’s the analogy to the pace of frontier model development: contestants are forced to spend sums financial observers do not like, just to stay where they are, competitively. You cannot afford to stop investing because your competitors are investing.

And raising fixed costs in the form of regulation makes it harder for upstarts to catch up. So today’s market leaders get some spending relief while challengers face higher costs if they want to try and catch up. 

Brookings made essentially this point specifically about AI licensing, noting that licensing can reinforce the position of companies that already possess the necessary approvals. An academic analysis of foundation-model competition likewise warned that the early calls for regulation were coming from major industry participants and could raise entry barriers. 

What the industry says	Potential strategic effect for an incumbent
“Frontier AI is too dangerous to develop without government oversight.”	Might well favor closed model developers more than open source developers, which are a key market challenger.
“Companies above a capability threshold should be licensed.”	Turns massive compute, safety teams and regulatory expertise into barriers to entry.
“Models should undergo expensive third-party testing.”	Adds fixed costs that a well-funded incumbent can absorb more easily than a startup.
“Developers must maintain sophisticated security and monitoring systems.”	Favors companies that already have large compliance and security organizations.
“We need standardized safety evaluations before deployment.”	Slows the frequency with which new models can be released and potentially reduces price competition.
“The industry should coordinate on safety standards.”	Can reduce the incentive for an incumbent to engage in an expensive capability race—although coordination among competitors raises antitrust issues.
“Government should establish clear rules rather than allowing a patchwork of state laws.”	A national framework can be easier for large incumbents to navigate than 50 different regimes. OpenAI has explicitly advocated harmonized national standards for this reason. (OpenAI)
“Only the most capable systems need stringent controls.”	Lessens market leader competitive threats by subjecting all to constraints.

The “regulatory moat” strategy would make it expensive and difficult for anybody else to become a frontier competitor.

The preferred rules would therefore tend to emphasize:

• licensing

• minimum compute/security requirements

• mandatory evaluations

• expensive certification

• reporting

• government audits

• liability restrictions on open-weight models

• controls on access to advanced chips.

An  “arms-race brake” strategy also applies. The objective isn't to eliminate competitors but to make the pace of competition more manageable.

Investment demands slow If every frontier company knows that once a model crosses a particular capability threshold it must:

• undergo independent evaluation

• demonstrate cybersecurity

• document dangerous capabilities

• establish monitoring

• satisfy government requirements

• perhaps obtain approval before deployment.

That should, in theory, lower the financial returns for the next N unit of performance enhancement, which is what frontier-model developers already are seeing. 

But that all works only when regulation slows all contestants equally. If only firms in one country agree to the regulation, while those in other countries do not have to comply, the regime doesn’t work.

From a leading supplier perspective, there is an incentive to support a regulatory regime that raises the fixed cost of frontier development and slows the competitive capability race, provided that the regulatory burden falls disproportionately on potential entrants and foreign competitors rather than on themselves.

If that can be accomplished, market leaders gain:

• a competitive moat

• more capital investment discipline

• Public policy legitimacy (“we are promoting safety, not protecting our market leadership”)

A company doesn't have to fabricate AI risks for regulation to serve its competitive interests.

Is California's Antitrust Lawsuit Against Paramount Skydance Near Resolution?

Is the California lawsuit trying to block the Paramount Skydance merger with Warner Brothers Discovery about to be settled? One might hope so, though some interests might not believe they benefit.

Given the “nuclear option” Paramount could exercise in moving its studios out of Los Angeles to another state, the city and state would benefit from keeping a key firm in a key industry. Paramount itself would prefer not to incur further delay and costs.

Sure, key competitors might prefer continued litigation. But both parties to the lawsuit have big reasons to settle. Paramount wants to execute on its business vision. California wants to keep a key firm in a key industry from moving.

So far, even though much actual production already has left California, studio headquarters and production lots remain based in the Los Angeles area. That matters as video content remains a vital part of the region's economy.  

source: Associated Press

The antitrust argument has been that the merger would give the merged company too much power in the video content value chain. That seems at least questionable. 

One high-level example is the oft-cited observation that If a consumer pays $100 at the box office:

• $45–50 ultimately remains with the theater

• $50–55 goes to the film distributor/studio.

Looking only at the content part of that example, owners of studios with distribution rights represent a bit more than half of revenue shared for exhibition, while theater owners get a bit less than half. But that isn’t the full story of theatrical exhibition revenues and profits. 

Movies represent huge risk, as it is not unusual for the top 10 movies distributed in a year to earn as much as 54 percent of all revenue. In other words, a small number of titles generate disproportionate revenue, which is why we see so many “existing franchise” titles issued. 

source: Statista

Still, it can be argued that talent (actors) and production workers receive about 20 percent of movie revenues; theaters perhaps the same and other participants also in the 20-percent range, with studios generally getting 35 percent of revenue in the broader video value chain that includes streaming and linear TV elements.  

The adage that movie theaters make their profits on popcorn is largely correct. Ticket revenue is shared with the studio, but concessions largely belong to the theater.

Theater chain AMC Entertainment in 2025 generated $2.65 billion of admissions revenue and $1.67 billion of food-and-beverage revenue, for example, according to data filed with the Securities and Exchange Commission by AMC Entertainment. 

And studio gross revenue is not profit, as production, talent, marketing costs, financing and overhead consume up to 90 percent of gross revenue, leaving perhaps five percent to 10 percent as expected profit, overall. 

Theatrical $100	Approximate share
Theater	$45–50
Studio/distributor gross	$50–55
— production/talent	~20–25
— marketing/distribution	~10–15
— studio overhead/financing	~5–10
— studio economic profit	~5–10

In the broader value chain that includes video streaming and linear TV, other elements come into play, such as advertising and subscription revenues. 

Warner Brothers Discovery in 2025 reported revenues of about $17.66 billion, composed of :

• $6.33 billion advertising revenue

• $9.82 billion distribution revenue (paid by streaming and linear networks)

• $1.20 billion content revenue. 

Content itself was a relatively small part of total revenue. The point might be that revenue shares are highly distributed, even if studios and a class of participants might claim the largest single shares in the value chain. 

Even more complicating is the fact that participants often participate in multiple parts of the value chain. Netflix and Disney provide the best examples. 

Model	Content ownership	Distribution ownership	Main economic advantage
Independent producer	Yes	No	Creative/IP specialization
Traditional studio	Yes	Partial	IP + multiple distribution windows
Cable network	Some	Yes	Audience + advertising/carriage
Pure streaming distributor	No/limited	Yes	Customer relationship + scale
Netflix-style integrated platform	Yes	Yes	Content + audience + data + global scale
Fully integrated Disney-like model	Yes	Yes	Multiple windows + franchises

The point is that the video content value chain is quite complex, with lots of participants and lots of distributed revenue streams. It is not so clear that any single role exercises monopoly-style control of the whole value chain. Content matters, but so does distribution, in any of the key segments (theatrical release, linear TV or streaming.

Lots of AI Regulations are Conceivable; Few Will Address Existential Threats

One problem with calls for “regulating artificial intelligence” is that it is not entirely clear what should be done, especially on the core issue of models becoming autonomous, in the context of nation state competition. 

The key idea of one approach known as “Mutual Assured AI Malfunction” is that nation state actors pursuing a monopoly on superintelligent AI should face the realistic prospect that rivals will detect and disable a destabilizing project before it matures. 

That is a specific problem, separate from general controls on autonomous AI use that might apply to all instances of autonomous action. 

MAIM sits within a larger conversation about whether AI competition more closely resembles nuclear arms races or something else. It rejects the idea that global pauses are possible or  politically unrealistic. Instead it treats great-power rivalry as inevitable.

The objective is creation of a default strategic environment in which no power can safely race to an unbridled advantage without expecting its own effort to be maimed. 

MAIM remains a theoretical and strategic proposal rather than formal policy in analyses of United States–China AI competition, for example. 

Critics will note limitations in the analogy to mutually assured destruction, which likewise prevented nuclear war by essentially ensuring that any use of strategic nuclear weapons would result in destruction of whichever nation chose to use it. So strategic nuclear weapons essentially become unusable. 

Nuclear weapons are passive instruments under human command chains.  Autonomous or superintelligent AI is an independent optimizing agent that may pursue goals orthogonal to its creators’ aims.

Nor are rogue actors limited to nation states. Once software exists, copying and iterating is cheap. This makes exclusive control or reliable sabotage harder. Non-state or rogue actors will not face the same mutual-vulnerability constraints that bind nuclear powers.

AI catastrophe scenarios can be harder to calculate in advance and may not leave clear “retaliatory” options once triggered. 

Incentives and actors are more heterogeneous. Nuclear deterrence primarily involved a small number of states with high stakes in regime survival. AI involves commercial firms, researchers, and diffuse national programs whose short-term competitive or profit motives can outweigh distant shared risks. 

So coordination problems are harder. Also, opaque model behavior can erode the signaling and crisis stability that nuclear deterrence relies on.

The point is that deterrence of this sort will be challenging. We won’t always agree on what moves are “destabilizing.” We will not have perfect transparency so there will always be some amount of ambiguity.

Sure, all sorts of “regulations” might also be proposed, but most of them do not directly involve controlling autonomous system misuse. 

That is not to say such other measures are unimportant. But they do not address concerns about existential AI threats posed by autonomous or super-intelligent systems. 

And the “easier” measures might not be directly effective in addressing autonomous system risks, either interstate or intrastate. 

So the “danger” is that politicians under pressure to “do something” will make a lot of noise about issues that arguably are useful, without addressing the “existential” dangers people worry about. 

In that sense, AI autonomy and its existential threats are no easier to solve than many other thorny social problems. 

Approach	What government would actually do	Main benefit	Main drawback / risk	Practicality
Apply existing laws	Use consumer protection, fraud, discrimination, privacy, securities, product-liability, employment and criminal laws when AI causes ordinary legally cognizable harms	Immediate and technology-neutral. Doesn't require government to predict which AI capabilities will matter	Existing laws may not fit novel harms; enforcement is often after the damage occurs	Very high
Mandatory disclosure, transparency	Require companies to disclose model capabilities, limitations, training-data summaries, evaluations, ownership, incidents, etc.	Gives customers, regulators and researchers information without directly controlling development	Disclosure can become expensive paperwork; sensitive information can reveal IP or security vulnerabilities; disclosures don't necessarily make systems safer	Very high
Independent testing, audits	Require frontier or high-risk systems to undergo standardized safety, cybersecurity, bias, privacy or performance testing before deployment	Creates an external check on company self-assessment; potentially catches problems before deployment	Hard to define meaningful tests for rapidly changing models; auditors can become a new bureaucracy; "passing" a benchmark may create false confidence	High
Risk-management standards	Require companies to maintain documented processes for identifying, measuring and mitigating AI risks—similar to financial, aviation or cybersecurity risk management	Regulates process rather than a particular technology; adaptable as AI changes	Companies can satisfy procedures without actually reducing risk; standards can become box-checking exercises	Very high
Incident reporting	Require developers/deployers to report serious AI failures, security breaches, dangerous behavior or loss of control to regulators	Creates a feedback system for regulators and researchers; particularly useful when risks are initially poorly understood	Companies may under-report; determining what constitutes a "serious" incident is difficult; reporting could expose security-sensitive information	High
Liability, duty of care	Make developers or deployers financially responsible when AI causes foreseeable harm, potentially through negligence, product liability or a specific AI duty of care	Uses economic incentives rather than prescriptive technical rules; encourages companies to internalize externalities	Liability can discourage experimentation; causation can be extremely difficult when many parties contribute to an AI system	High, especially sector-by-sector
Sector-specific regulation	Give FDA, FAA, financial regulators, healthcare regulators, employment regulators, etc. authority over AI used in their domains	Probably the most natural approach because the risk of an AI system depends heavily on what it does	Creates multiple regulatory regimes; can slow adoption and produce inconsistent requirements	Very high
Prohibit particular applications	Ban specified uses—e.g., certain forms of biometric surveillance, manipulation, autonomous weapons, discriminatory decision-making or fraud	Very clear red lines; prevents particularly unacceptable uses regardless of technical sophistication	Defining the prohibited category is difficult; legitimate and harmful uses can look technically similar; enforcement can be evaded	High for narrow uses
High-risk classification	Create categories such as "high-risk AI" and impose enhanced testing, documentation, human oversight and monitoring on them	Focuses regulation where potential harm is greatest rather than regulating every chatbot	Classification disputes; innovation may migrate around regulatory boundaries	High
Frontier-model requirements	Impose additional obligations on the largest/most capable models—risk evaluations, cybersecurity, incident reporting, red-team testing, safety plans, etc.	Concentrates regulation on the relatively small number of firms developing frontier systems	Capability thresholds quickly become obsolete; could protect incumbents by making it harder for startups to compete	Medium-high
Government licensing / pre-approval	Require a company to obtain government permission before training or releasing models above a specified capability/compute threshold	Gives government the strongest ability to stop genuinely dangerous systems before deployment	Extremely intrusive; government must determine what constitutes dangerous capability; potentially slows beneficial innovation and entrenches existing firms	Low-medium
Regulate compute	Require reporting/licensing for very large AI-training runs or data centers; track high-end chips and compute clusters	Targets the physical bottleneck underlying frontier AI rather than trying to regulate every model	Compute becomes cheaper/more distributed; companies can optimize algorithms or move overseas; difficult to distinguish benign from dangerous compute	Medium
Chip/export controls	Restrict advanced AI chips, semiconductor equipment or model weights from particular countries/entities	Useful when the objective is national security, because chips are tangible and controllable	Doesn't directly address domestic AI harms; encourages substitution, stockpiling and indigenous foreign capacity	High for national-security objectives
Competition, antitrust policy	Challenge acquisitions, exclusive cloud/model/chip arrangements, discriminatory access, tying and potentially vertical integration	Addresses the possibility that AI becomes concentrated among a handful of firms and preserves competitive access to compute/models	Antitrust remedies are slow; breaking up or restricting efficient integration could reduce economies of scale	High
Interoperability, portability	Require APIs, data portability, model portability or interoperability between AI services	Reduces switching costs and makes it easier for startups to compete	Standardization can freeze technology prematurely and potentially reduce security	Medium
Copyright, training-data rules	Require licensing, compensation, opt-outs, attribution or disclosure concerning copyrighted training material	Protects creators and establishes property rights around a major AI input	Could substantially increase model costs; licensing billions of works is technically difficult; may advantage firms with large legal budgets	Medium
Privacy, data regulation	Restrict collection and use of personal data for training and inference; require consent, minimization and deletion	Addresses a concrete existing harm without regulating AI as such	Reduces useful training data; enforcement and cross-border data issues are difficult	High
AI-generated-content labeling	Require disclosure/watermarking of synthetic images, video, audio or text	Helps people distinguish synthetic from human-created material and can improve provenance	Watermarks can be removed; detection becomes harder as models improve; doesn't solve persuasion/misinformation itself	Medium-high
Government procurement rules	Government agencies purchase only AI satisfying specified security, transparency, privacy or performance requirements	Government can use its enormous purchasing power without regulating the entire private economy	Primarily affects government suppliers; procurement standards can become politicized or overly restrictive	Very high
Taxes, fees on AI or compute	Tax AI usage, high-end compute, AI profits or automation; use revenue for worker transition, social insurance, etc.	Addresses distributional effects while allowing firms to continue innovating	Difficult to identify the tax base; could discourage productive investment and push activity offshore	Medium
Worker-transition policies	Expand unemployment insurance, retraining, wage insurance, portable benefits or education rather than restricting AI	Deals with the consequences of AI rather than trying to stop AI; preserves productivity benefits	Expensive; difficult to target assistance toward genuinely displaced workers	Very high
International agreements	Establish common standards for dangerous capabilities, testing, incident reporting, chip controls and military uses	Reduces regulatory arbitrage and makes some global risks manageable	Extremely difficult because countries have different economic and national-security interests	Low-medium, but potentially important