paper_id: ai-race-isnt-real

paper_title: The AI Race Isn’t Real

authors: Yonathan A. Arbel and Matthew Tokson

citation: Yonathan A. Arbel & Matthew Tokson, The AI Race Isn’t Real, Lawfare (2026).

source_type: unpaginated Lawfare online essay

printed_pages: unpaginated

pdf_pages: unpaginated

section: Opening and Thesis

claim: The race-against-China narrative has become a cross-institutional justification for weakening domestic AI safeguards

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “Opening and Thesis,” that the Trump administration’s safety rollback and AI Action Plan, congressional arguments against regulation, and industry opposition to state rules all invoke a race with China. The rhetoric treats speed and scale as overriding goals and recasts ordinary safety requirements as impediments to national survival. This is significant because a metaphor is performing concrete regulatory work across the executive branch, Congress, the military, and private lobbying. It connects to AI deregulation, U.S.–China competition, regulatory framing, industrial lobbying, national security, and policy agenda setting.

significance: The opening identifies the race frame as a policy mechanism rather than a harmless description of technological competition.

connections:
AI deregulation
U.S.–China competition
regulatory framing
industrial lobbying
national security
policy agenda setting

limitations: The cited statements demonstrate the frame’s influence but do not prove that every deregulatory decision has the same motive or effect.

evidence_summary: The essay opens with executive orders, the AI Action Plan, military directives, congressional statements, and industry lobbying that use race rhetoric to oppose safety or regulatory constraints.

review_status: machine-drafted-source-checked


claim: The AI-race frame is both descriptively mistaken and normatively dangerous

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “Opening and Thesis,” that AI competition lacks a finish line, offers weak prospects for durable first-mover dominance, and is distorted by rapid knowledge diffusion. Normatively, the race frame rewards unsafe speed, destabilizes deterrence, and narrows policy choices into a zero-sum sprint. This is significant because the case against racing does not depend on denying all value to American leadership; it challenges the move from marginal advantage to categorical imperative. It connects to technological competition, arms-race theory, safety regulation, deterrence, first-mover advantage, and cost-benefit analysis.

significance: The two-part thesis distinguishes a critique of the metaphor’s factual premises from a critique of the behavior the metaphor induces.

connections:
technological competition
arms-race theory
safety regulation
deterrence
first-mover advantage
cost-benefit analysis

limitations: The authors accept that American AI superiority can be beneficial at the margin and do not argue for abandoning investment or competition.

evidence_summary: The opening expressly labels the race idea descriptively flawed and normatively defective and previews its effects on diffusion, markets, safety, strategy, and policy choice.


claim: AI competition has no flag-on-the-Moon endpoint and therefore cannot be won like the space race

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “The Descriptive Failure of the AI Arms Race Concept,” that the space-race analogy smuggles in a decisive finish line that AI lacks. Military AI is an ongoing accumulation of capabilities, and temporary technical leadership does not create a stable end state in which one country permanently wins. This is significant because a policy organized around crossing a nonexistent finish line encourages perpetual acceleration without specifying what success would mean. It connects to the space race, arms-race metaphors, technological trajectories, policy objectives, military AI, and strategic planning.

significance: The absence of an endpoint attacks the most intuitive implication of the race metaphor.

connections:
space race
arms-race metaphors
technological trajectories
policy objectives
military AI
strategic planning

limitations: The claim concerns durable victory, not the possibility that temporary leads can produce meaningful tactical, economic, or diplomatic advantages.

evidence_summary: The essay contrasts the space race’s clear lunar milestone with an open-ended AI competition that continually generates new capabilities.


claim: A permanent military-AI victory would require domination or destruction radically unlike the benign idea of winning a technological race

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “The Descriptive Failure of the AI Arms Race Concept,” that the only durable way to prevent a capable rival from catching up would be conquest, destruction of its research capacity, continuous coercive monitoring, or a credible threat of annihilation. Those are projects of permanent subordination, not an ordinary finish-line victory. This is significant because race language hides the extreme geopolitical end state that lasting military dominance would actually require. It connects to preventive war, domination, nuclear escalation, democratic accountability, euphemistic framing, and international norms.

significance: The claim forces policymakers to state the coercive implications of permanent victory openly rather than shelter them behind a sporting metaphor.

connections:
preventive war
domination
nuclear escalation
democratic accountability
euphemistic framing
international norms

limitations: The authors present this as a conceptual implication of lasting dominance, not as evidence that current policymakers intend conquest or annihilation.

evidence_summary: The essay enumerates the coercive acts required for durable victory and argues that they would be catastrophic, immoral, and far removed from ordinary race rhetoric.


claim: Rapid diffusion can make frontier acceleration pull competitors forward rather than widen the leader’s advantage

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “The Descriptive Failure of the AI Arms Race Concept,” that AI knowledge is unusually leaky through publication, open weights, APIs, reverse engineering, distillation, and employee mobility. A breakthrough by the leader can reveal a feasible direction and supply tools or outputs that accelerate followers. This is significant because moving faster may shorten rather than lengthen the time before a rival acquires comparable capability. It connects to knowledge spillovers, diffusion, reverse engineering, model distillation, open-source AI, and catch-up growth.

significance: The diffusion mechanism reverses the ordinary race intuition that acceleration necessarily increases separation.

connections:
knowledge spillovers
diffusion
reverse engineering
model distillation
open-source AI
catch-up growth

limitations: Not all knowledge is equally transferable, and advantages grounded in capital-intensive industrial capacity or tacit manufacturing knowledge may persist longer.

evidence_summary: The essay contrasts AI’s transferable algorithms and outputs with less leaky technologies such as EUV lithography and stealth manufacturing.


claim: Recent AI development illustrates diffusion through publication, open models, reverse engineering, and industrial-scale distillation

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “The Descriptive Failure of the AI Arms Race Concept,” that Google’s publication of the transformer architecture helped enable OpenAI, Meta’s Llama supported DeepSeek variants, and firms have repeatedly reverse-engineered or distilled rivals’ systems. They cite reported industrial-scale campaigns involving millions of exchanges as evidence that model behavior itself can become a training resource. This is significant because the competitive externality arises from releasing or exposing capability, even when proprietary details remain secret. It connects to transformers, Llama, DeepSeek, OpenAI, model extraction, and innovation spillovers.

significance: The examples give institutional content to the otherwise abstract claim that AI knowledge leaks across national and corporate boundaries.

connections:
transformers
Llama
DeepSeek
OpenAI
model extraction
innovation spillovers

limitations: The examples show channels of diffusion but do not establish identical transfer rates for every model, technique, or military application.

evidence_summary: The article recounts transformer publication, Llama-based distillation, reciprocal reverse engineering, and documented large-scale extraction campaigns.


claim: OpenAI’s o1 release reduced rivals’ uncertainty about test-time reasoning and redirected competition toward an imitable algorithmic target

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “The Descriptive Failure of the AI Arms Race Concept,” that the release of o1 disclosed more than a product: it confirmed that test-time inference could scale into a powerful reasoning paradigm. Even without full technical disclosure, competitors could redirect research toward a validated target, followed months later by DeepSeek’s R1. This is significant because demonstrating that a path works can dissipate a lead by converting uncertainty into focused imitation and innovation. It connects to o1, test-time inference, DeepSeek R1, information revelation, research direction, and algorithmic competition.

significance: The o1 example shows how a leader can accelerate catch-up simply by proving a capability is achievable.

connections:
o1
test-time inference
DeepSeek R1
information revelation
research direction
algorithmic competition

limitations: Temporal sequence does not by itself prove that o1 caused R1, and the essay uses the episode as an illustration rather than a controlled causal study.

evidence_summary: The essay describes o1’s paradigm confirmation, subsequent competitor redirection, and R1’s appearance four months later.


claim: Privately operated, networked AI infrastructure is a comparatively soft espionage target, and race pressure can worsen that vulnerability

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “The Descriptive Failure of the AI Arms Race Concept,” that frontier models reside in commercial networked data centers rather than only in hardened classified facilities. Sophisticated state actors can target corporate systems, while pressure to move quickly can weaken cybersecurity discipline; known breaches suggest that additional theft may go undetected. This is significant because national acceleration can transfer capability to the rival it is supposed to outpace. It connects to cyber espionage, frontier laboratories, trade-secret theft, critical infrastructure, security-by-design, and state-sponsored intrusion.

significance: The cybersecurity channel reinforces the claim that faster domestic development can shorten a competitor’s path to comparable systems.

connections:
cyber espionage
frontier laboratories
trade-secret theft
critical infrastructure
security-by-design
state-sponsored intrusion

limitations: The essay cites limited public breach evidence and acknowledges that the full incidence of successful or attempted theft is not observable.

evidence_summary: The authors compare commercial model infrastructure with classified weapons facilities and point to inadequate safeguards and a known major-company breach.


claim: Foundation models currently show weak signs of the network effects and durable moats needed for winner-take-all economic dominance

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “The Descriptive Failure of the AI Arms Race Concept,” that users increasingly switch among models based on price, latency, convenience, capability, and style, while leaderboard positions change frequently. Industry observations about commoditization and the absence of a moat therefore caution against treating a temporary frontier lead as an appropriable natural monopoly. This is significant because the economic case for emergency deregulation is weaker when advantages are contestable and multidimensional. It connects to network effects, natural monopoly, commoditization, switching, competitive moats, and foundation-model markets.

significance: The market-structure analysis separates ordinary industrial advantage from the durable dominance implied by race rhetoric.

connections:
network effects
natural monopoly
commoditization
switching
competitive moats
foundation-model markets

limitations: The authors acknowledge uncertainty and possible future differentiation in latency, cost, capability, or autonomy; durable moats could still emerge.

evidence_summary: The essay discusses model interchangeability, shifting leaderboards, industry commentary, and scholarship questioning AI natural monopoly.


claim: Recursive self-improvement does not guarantee a permanent lead because exponential growth is contestable and physical bottlenecks remain

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “The Descriptive Failure of the AI Arms Race Concept,” that recursive self-improvement could compound an early advantage only if capability growth remains sufficiently exponential. Technological diminishing returns, log-linear scaling, chips, industrial capacity, and electricity can slow the cycle, and competitors with physical-resource advantages can enter it as well. This is significant because the strongest theoretical case for a decisive first mover rests on assumptions rather than an automatic consequence of self-improving software. It connects to recursive self-improvement, scaling laws, diminishing returns, compute bottlenecks, electricity, and industrial capacity.

significance: The argument limits a common route from temporary technical leadership to claims of irreversible dominance.

connections:
recursive self-improvement
scaling laws
diminishing returns
compute bottlenecks
electricity
industrial capacity

limitations: The essay does not rule out rapid or discontinuous self-improvement; it argues that durable exponential advantage should not be assumed.

evidence_summary: The authors identify growth-shape and physical-resource conditions that would have to hold for recursive self-improvement to lock in a leader.


claim: AI development is a continuing multidimensional competition because users value more than maximum benchmark intelligence

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “The Descriptive Failure of the AI Arms Race Concept,” that users choose systems based on cost, latency, style, autonomy, and reliability as well as raw capability. Compute constraints can even pressure firms to innovate in efficiency, creating distinct competitive advantages rather than a single ordered leaderboard. This is significant because there is no unitary track on which one laboratory or country can permanently cross first. It connects to product differentiation, heterogeneous preferences, efficiency innovation, benchmarking, market competition, and user welfare.

significance: The multidimensional account undermines the idea that one capability score defines national or commercial victory.

connections:
product differentiation
heterogeneous preferences
efficiency innovation
benchmarking
market competition
user welfare

limitations: Some general capabilities may still matter disproportionately, especially in military or scientific applications.

evidence_summary: The essay lists non-capability attributes users value and describes ongoing competition across multiple dimensions without a settled winner.


claim: American AI advantage should be pursued through ordinary industrial policy and cost-benefit regulation rather than a singular race imperative

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “The Descriptive Failure of the AI Arms Race Concept,” that a sustainable American lead may depend on chips, advanced lithography, data centers, electricity, and a strong industrial base. But AI leadership should face the same balancing of innovation benefits and unregulated-market costs applied to drugs, power plants, chemicals, and cars. This is significant because race framing converts deregulation from one contestable tool into an asserted necessity and suppresses ordinary policy comparison. It connects to industrial policy, semiconductor capacity, energy infrastructure, cost-benefit regulation, technology governance, and regulatory exceptionalism.

significance: The proposal preserves a role for national investment while rejecting AI’s exemption from normal regulatory analysis.

connections:
industrial policy
semiconductor capacity
energy infrastructure
cost-benefit regulation
technology governance
regulatory exceptionalism

limitations: The essay sketches rather than specifies the optimal mix of industrial subsidies, export controls, safety mandates, and competition policy.

evidence_summary: The authors distinguish infrastructure-based leadership from race-driven deregulation and compare AI with other advanced industries subject to safety rules.


section: Opening and Descriptive Conclusion

claim: American AI leadership should be defined by usefulness, capability, reliability, and safety rather than brute capability alone

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, in the opening and descriptive conclusion, that the United States can seek returns from leadership without defining success as maximal model capability achieved at maximal speed. A more attractive position is to supply systems that are powerful but also useful, reliable, and safe. This is significant because it replaces a negative call to stop racing with a positive standard for competitive excellence. It connects to trustworthy AI, quality competition, reliability, safety engineering, American leadership, and innovation policy.

significance: The alternative metric gives policymakers and firms a way to value leadership without accepting the race frame’s deregulatory logic.

connections:
trustworthy AI
quality competition
reliability
safety engineering
American leadership
innovation policy

limitations: The essay does not provide operational metrics for usefulness, reliability, or safety, leaving tradeoffs among those dimensions for future work.

evidence_summary: The opening previews and the descriptive section restates an alternative concept of leadership based on capability, effectiveness, reliability, and safety.


section: The Dangers of the AI Race Concept

claim: Belief that a rival is nearing decisive AI military superiority can create incentives for catastrophic preemption even when superiority is uncertain

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “The Dangers of the AI Race Concept,” that a state fearing imminent subjugation by an AI-enabled superweapon may prefer a strike with existing forces to waiting for the threat to mature. Yet even radically advanced AI may be unable to neutralize dispersed or nonnetworked nuclear forces, so both the fear of dominance and confidence in disarmament can be mistaken. This is significant because race beliefs can destabilize deterrence through miscalculation before any decisive capability exists. It connects to preventive war, nuclear deterrence, strategic stability, misperception, second-strike capability, and AI weapons.

significance: The argument identifies a pathway from speculative capability claims to immediate great-power escalation risk.

connections:
preventive war
nuclear deterrence
strategic stability
misperception
second-strike capability
AI weapons

limitations: The scenario is a strategic risk model, not a prediction that a particular state will launch a preemptive strike.

evidence_summary: The dangers section describes preemption incentives and notes the physical resilience of many nuclear systems against cyber or AI disruption.


claim: Accelerated military AI can diffuse dangerous capabilities to rogue states and non-state actors while increasing civilian harm from autonomous weapons

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “The Dangers of the AI Race Concept,” that advanced military applications are unlikely to remain confined to the leading great powers. As with nuclear technology, capabilities can spread to smaller states and non-state actors, while proliferating autonomous systems create additional risks of unintentional civilian injury. This is significant because even a temporary lead can impose global externalities that outlast or escape the strategic contest that produced it. It connects to weapons proliferation, rogue states, non-state actors, autonomous weapons, civilian protection, and dual-use diffusion.

significance: The proliferation argument widens the relevant cost-benefit analysis beyond the two countries imagined by race rhetoric.

connections:
weapons proliferation
rogue states
non-state actors
autonomous weapons
civilian protection
dual-use diffusion

limitations: The comparison with nuclear diffusion is analogical, and the speed, accessibility, and destructive profile of AI capabilities may differ substantially.

evidence_summary: The essay links advanced military AI to proliferation beyond great powers and to accidental harms as autonomous systems spread.


claim: The AI-race metaphor can operate as a hyperstition that manufactures a self-sustaining security dilemma

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “The Dangers of the AI Race Concept,” that describing AI development as a race may make the description true through the behavior it inspires. Each side’s accelerated investment, even if defensive, signals hostile intent to the other; reciprocal escalation then supplies fresh evidence that the race is urgent. This is significant because policymakers are not merely observing an external competitive fact but can construct the dangerous equilibrium they cite as justification. It connects to hyperstition, the security dilemma, signaling, arms-race dynamics, self-fulfilling prophecy, and international relations.

significance: The claim makes rhetoric endogenous to strategic reality and explains how a contest can become self-reinforcing.

connections:
hyperstition
security dilemma
signaling
arms-race dynamics
self-fulfilling prophecy
international relations

limitations: Security competition may have independent material causes, so abandoning the metaphor alone would not eliminate all rivalry or mistrust.

evidence_summary: The essay explains how defensive acceleration is read as offensive intent and how reciprocal investment sustains the belief in a race.


claim: Race pressure makes AI systems more brittle by rewarding speed, disrupting normal safety incentives, and giving firms an alibi for shortcuts

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “The Dangers of the AI Race Concept,” that frontier development would continue under ordinary competition while firms and regulators balanced gains from capability against testing, safeguards, and liability. A race posture instead makes delay look like defeat and permits laboratories to characterize safety investment as a strategic handicap. This is significant because the frame can degrade system quality even if it does not increase the underlying technical difficulty of safety. It connects to safety culture, organizational incentives, brittle systems, liability, testing, and regulatory races to the bottom.

significance: The safety critique identifies how political framing changes institutional incentives inside frontier laboratories.

connections:
safety culture
organizational incentives
brittle systems
liability
testing
regulatory races to the bottom

limitations: The authors do not claim that normal commercial competition is safe by itself; public policy is still needed to create conditions for adequate testing and safeguards.

evidence_summary: The essay contrasts a regulated competitive equilibrium with a race dynamic that rewards speed and supplies a justification for deprioritizing safety.


claim: Military competition pressures humans to overdelegate decisions to AI systems in order to exploit machine speed

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “The Dangers of the AI Race Concept,” that sound principal-agent design normally mixes delegation with oversight. In a military race, the perceived need to act at machine speed pushes decision-makers toward autonomous authority at the expense of meaningful human review, a tendency they see previewed in autonomous targeting during the Russia–Ukraine war. This is significant because competitive speed can convert a governance choice into an operational necessity before control problems are solved. It connects to principal-agent theory, human oversight, autonomous weapons, machine speed, military command, and meaningful human control.

significance: The overdelegation mechanism links geopolitical competition to the internal governance of AI agents.

connections:
principal-agent theory
human oversight
autonomous weapons
machine speed
military command
meaningful human control

limitations: The battlefield example is a preview rather than proof that all advanced military AI will be deployed without effective human controls.

evidence_summary: The essay applies principal-agent risks to military AI and argues that speed competition biases the delegation-oversight balance toward autonomy.


claim: Racing toward superintelligence before solving weaker alignment problems creates catastrophic risk rather than a sensible competitive advantage

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, under “The Dangers of the AI Race Concept,” that superintelligence could confer extraordinary strategic benefits but would exceed human abilities in domains where current alignment remains brittle and incomplete. Deploying such systems before reliable control is comparable to accumulating immensely dangerous weapons before developing safeguards against accidental use. This is significant because the very capability that makes “winning” attractive also raises the consequence of sacrificing caution to speed. It connects to superintelligence, alignment, catastrophic risk, control problems, nuclear analogy, and existential risk.

significance: The argument directly rejects the idea that racing is safer merely because a friendly state might reach transformative capability first.

connections:
superintelligence
alignment
catastrophic risk
control problems
nuclear analogy
existential risk

limitations: The analysis is conditional on uncertain superintelligence trajectories and does not assert a precise probability or timeline for catastrophic outcomes.

evidence_summary: The essay acknowledges the potential strategic value of superintelligence but emphasizes unsolved alignment and the recklessness of deploying vastly more capable systems first.


section: Conclusion

claim: The benefits of temporarily leading AI development are smaller and less durable than race advocates imply, while the downside risks can be global and catastrophic

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, in the conclusion, that diffusion and contestable markets make the rewards of “winning” temporary, whereas racing can increase rogue-actor attacks, great-power war, civilian harm, and human extinction risk. The proper response is therefore not simply to run faster but to question the race frame that aggregates unlike risks into a single imperative. This is significant because it changes the relevant comparison from relative national speed to the total social consequences of the competitive posture. It connects to systemic risk, temporary advantage, global externalities, catastrophic harm, strategic competition, and policy reframing.

significance: The conclusion brings the descriptive and normative critiques together in a single comparative-risk judgment.

connections:
systemic risk
temporary advantage
global externalities
catastrophic harm
strategic competition
policy reframing

limitations: The essay does not monetize or probabilistically aggregate the listed benefits and harms; its conclusion is a qualitative policy argument.

evidence_summary: The closing paragraphs contrast limited, temporary competitive gains with harms ranging from proliferation and war to extinction.


claim: Abandoning the finish-line metaphor reopens cooperative and regulatory tools that race logic makes appear impossible

thick_description: Professors Yonathan A. Arbel and Matthew Tokson claim, in “The AI Race Isn’t Real,” an unpaginated Lawfare essay, in the conclusion, that once policymakers recognize there is no finish line, options obscured by zero-sum urgency become available again. These include cooperation on safety standards, measured pacing of frontier development, catalytic regulation, and international agreements governing military AI. This is significant because conceptual reframing expands the feasible policy set rather than merely slowing technological development. It connects to international cooperation, safety standards, frontier pacing, catalytic regulation, military-AI agreements, and positive-sum governance.

significance: The final move shows that rejecting race rhetoric is intended to enable alternative leadership strategies, not produce policy paralysis.

connections:
international cooperation
safety standards
frontier pacing
catalytic regulation
military-AI agreements
positive-sum governance

limitations: The essay identifies policy categories but leaves their institutional design, enforcement, and geopolitical feasibility to the forthcoming longer article and future work.

evidence_summary: The final paragraph lists cooperation, pacing, catalytic regulation, and military-AI agreements as options made visible by discarding the race frame.