How will open-source AI models (Llama, Mistral, DeepSeek) reshape the competitive landscape vs. closed models

When AI Recipes Become Free: What Happens to the Companies That Kept Them Secret?

April 10, 2026 | 131 nodes · 450 edges

↓ .md ↓ .db Take this into your AI — the full analysis + graph as markdown, ready to paste into ChatGPT, Claude, Gemini or any AI.

Based on analysis of a 131-node, 450-edge knowledge graph exploring how open-source AI models (Llama, Mistral, DeepSeek) are reshaping the competitive landscape against closed models.

Imagine a world where a handful of restaurants discovered a revolutionary way to cook food — a secret process that made everything taste incredible. Customers lined up. Prices were high. The restaurants made a lot of money, and nobody else could replicate what they did because the recipes were locked away.

Now imagine one of the biggest restaurant chains in the world — one that makes most of its money from advertising, not food — decided to give away its recipes for free. Not because it was feeling generous, but because if everyone uses its recipes, its way of cooking becomes the standard, and that’s good for its other businesses.

That is roughly the situation this knowledge graph describes. The “restaurants” are AI companies like OpenAI and Anthropic. The “recipe-giver” is Meta. And the free recipes are AI models like Llama. The graph tries to map out every consequence of that decision — who wins, who loses, what gets more complicated, and what nobody fully saw coming.

The single most connected node in the graph is called “Meta Open-Source Commoditization Strategy.” That is a fancy phrase for: Meta keeps releasing powerful AI models for free, on purpose, because it benefits Meta’s actual business.

Meta makes money from Facebook and Instagram ads. AI is expensive to run, and Meta needs to use a lot of it. If AI gets cheaper across the whole industry — if the “ingredients” become freely available and competition drives prices down — Meta benefits more than anyone. It does not need to sell AI; it just needs AI to be cheap.

So Meta funds the free recipe program with advertising money, releases the recipes publicly, and watches the market for AI services get more competitive and less expensive. The graph shows this explicitly: Meta’s social media business subsidizes the AI ecosystem, including the platforms that distribute everyone else’s models, not just Meta’s own.

The key insight here is that Meta is not the cause of AI getting cheaper — it is the transmission mechanism. It takes a dozen different forces (new efficiency techniques, research published by competitors, growing developer communities) and converts them into a single, sustained strategic push that affects the whole market.

Prices Are Falling Fast — But There Is a Puzzle

One of the clearest patterns in the graph is that the cost of using AI is dropping, and dropping quickly. More efficient model designs, cheaper hardware, more competition — all of these push prices down. The graph calls this the “LLM Token Deflation Race,” which just means: AI output is getting cheaper per unit, consistently, across the industry.

But here is the puzzle the graph does not resolve: when something gets dramatically cheaper, people often use a lot more of it. Electricity got cheaper and people bought more appliances. Cars got cheaper and people drove more miles. This is called the Jevons Paradox — the efficiency gain gets “eaten up” by increased demand.

The graph contains two competing chains of logic that both carry high confidence scores. One says: prices will keep falling toward zero as efficiency improves. The other says: as AI gets cheaper, new uses get unlocked, demand expands, and the total amount of AI computing being done actually grows, which puts a floor under prices.

Both chains are present, both are weighted heavily, and the graph does not pick a winner. Whether your AI bill goes down or stays roughly flat while you get more for it depends on which effect dominates — and that is genuinely an open question.

The Most Surprising Finding: Export Controls Made the Problem Worse

The United States restricted the sale of advanced computer chips to China. The intended effect was to slow down Chinese AI development by limiting access to the hardware required to train powerful models.

The graph documents what appears to have happened instead. Cut off from the best hardware, Chinese AI researchers had a strong incentive to find smarter, more efficient ways to use the hardware they could access. This led to breakthroughs in how AI models are designed — techniques like “Mixture of Experts” (where only part of the AI activates for any given task, saving computation) and aggressive compression methods that make models smaller without losing much capability.

Those techniques, once published or inferred, are not hardware-specific. Anyone can use them. The graph’s description of this is direct: export controls functioned as an “algorithmic innovation catalyst.” Restricting hardware access pushed Chinese labs toward software efficiency gains that then diffused globally.

The graph records the highest-confidence relationship in the entire dataset as a geopolitical one: China’s strategy of releasing capable open-source models is driving AI infrastructure adoption in the Global South — developing countries choosing which AI systems to build their technology on. That single relationship carries the highest edge weight in the graph (9.8 out of 10), higher than any purely technical connection.

NVIDIA Wins When AI Gets Cheaper (Counterintuitive)

If AI models get cheaper and more efficient, you might expect the company selling the expensive hardware used to run them to lose. NVIDIA’s chips are the dominant infrastructure for AI. If models need less computation, NVIDIA sells fewer chips, right?

The graph suggests the opposite dynamic is actually operating. When AI gets cheaper per use, more people use AI. More uses means more total computation, even if each individual use costs less. NVIDIA sells into total volume, not per-use cost. The graph labels this the “NVIDIA Open-Source Infrastructure Paradox” and connects it explicitly to the Jevons Paradox logic: efficiency expands the market, and NVIDIA benefits from a larger market.

This does not mean NVIDIA has no problems. The graph also records competitive pressure from AMD, geopolitical constraints from semiconductor policy, and strategic tension from its own role in the system. But on the core question of whether open-source AI hurts NVIDIA, the graph’s answer is: not necessarily, and possibly the reverse.

Closed Labs Keep Publishing Research That Helps Their Competitors

Closed AI labs — companies that keep their models proprietary — have a tradition of publishing research papers. They do this to attract talented researchers (who want academic credibility), to establish scientific priority, and to maintain a reputation in the field.

The graph identifies this as a structural mechanism for transferring capability to competitors. The published research explaining how to make models more efficient was used, directly and explicitly, by DeepSeek to build more capable models at lower cost. The publication behavior that serves closed labs’ talent acquisition goals is the same behavior that transfers architectural knowledge to anyone who reads the papers.

The graph represents this as structural, not accidental. It is not that closed labs made a mistake once — it is that the incentive to publish and the competitive risk of publishing are baked into the same decision, repeatedly.

The Data Moat Question Has No Answer Yet

Closed AI companies have something open-source models lack: enormous amounts of proprietary data, carefully labeled by humans, used to train and refine their models. The argument is that this “data moat” cannot be replicated cheaply, and it is what keeps frontier closed models ahead.

The graph contains roughly equal-weight arguments on both sides of this question. On one side: proprietary data compounds in value, fine-tuning on specialized data creates durable advantages, and human-labeled feedback is hard to replicate. On the other side: synthetic data (AI-generated training data distilled from frontier models) is getting better, open-source fine-tuning tools are widely available, and the ability to transfer knowledge from closed to open models through distillation is improving.

The graph records the contest without resolving it. Both the moat-building and the moat-eroding mechanisms are present and weighted comparably. The specific threshold question — at what point does synthetic data quality match human-labeled data closely enough to make proprietary datasets obsolete — is left open.

Safety Rules Are Being Established and Undermined at the Same Time

The graph maps a race between two processes: institutions trying to establish rules about what AI models can and cannot do, and the practical reality that open-weight models can have safety constraints permanently removed by anyone with the technical knowledge to do so.

When an AI model’s weights — its “recipe” in full detail — are released publicly, anyone can modify it. Safety restrictions can be stripped out. This is not reversible at the model level; once the weights are public, the ungoverned version can be copied and redistributed indefinitely.

The graph shows safety governance mechanisms being established (EU regulations, concerns about dangerous capability proliferation) while simultaneously being undermined by the practical impossibility of enforcing them on already-released weights, and further weakened by the dynamic where geopolitical competition makes restrictive safety standards a competitive liability in some contexts.

The graph does not predict which process wins. It identifies the race and shows that an external shock — tightening export controls, for instance — actually weakens governance effectiveness rather than strengthening it, because it drives algorithmic innovation in contexts with less safety oversight.

The Bottom Line

What the graph’s structure actually shows:

Meta is the most important single actor in the graph, not because it invented open-source AI, but because it is the mechanism through which many independent forces (chip efficiency, published research, developer ecosystems, geopolitics) get converted into sustained market-wide pressure on AI prices and proprietary advantage.

The commoditization of AI capability appears to be a relay process, not a single event — multiple independent forces feeding into a cascade that feeds into price pressure, which then feeds back into more commoditization.

The geopolitical dimension is more tightly coupled than the technical one. The strongest relationship in the entire graph is between China’s open-source AI strategy and infrastructure adoption in developing countries — not between any two technical mechanisms.

Three questions remain structurally unresolved: whether AI prices stabilize (Jevons) or continue falling (deflation), whether proprietary data remains a durable advantage or gets eroded by synthetic substitutes, and whether safety governance catches up to proliferation before open-weight releases make it practically moot. The graph contains the competing arguments for each — it does not adjudicate between them, because the outcome depends on rates of change that were not yet observable when the graph was constructed.

Structural Analysis: Open-Source AI vs. Closed Models Knowledge Graph

Key Findings

1. The Commoditization Cascade is a Structural Relay, Not a Cause

AI Capability Commoditization Cascade (31 connections) occupies a relay position: it receives inputs from distinct upstream clusters (technical efficiency gains, ecosystem effects, strategic moves) and distributes them as downstream consequences (market stratification, value migration, safety concerns). It is not itself an originating force — it converts specific events (DeepSeek shocks, LoRA economics, MoE efficiency) into broad market-wide propagation. The highest-weight edges entering it include MoE Sparse Activation Efficiency --[amplifies, w=8.9]--> and Llama Ecosystem Gravity Well --[accelerates, w=8]-->.

2. Meta Open-Source Commoditization Strategy is the Graph’s Primary Transmission Mechanism

With 40 connections, it receives inputs from Meta’s business model (Meta Social Media Subsidy Model --[funds]-->), technical enablers (Llama Ecosystem Gravity Well, Open-Source Talent Acquisition Flywheel), and the broader cascade (AI Capability Commoditization Cascade --[amplifies, w=8]-->). Its outgoing edges span competitors’ profitability (--[amplifies, w=9]--> Closed Model Profitability Structural Crisis), safety governance (--[amplifies]--> Open-Source AI Safety Defection Problem), and geopolitics (--> China Open-Source AI Soft Power Gambit via multiple paths). No other node spans as many distinct subsystems simultaneously.

3. LLM Token Deflation Race is Primarily a Convergence Sink

LLM Token Deflation Race (36 connections) receives amplifying edges from over 20 distinct nodes and sends meaningful output to only 3 primary targets: Closed Model Profitability Structural Crisis, Bimodal AI Market Stratification, and CBRN Capability Proliferation Irreversibility. This asymmetry identifies it as a convergence accumulator — a place where competitive, technical, and strategic forces register as a single observable market outcome (price pressure), rather than a mechanism that propagates forward.

4. The Jevons Paradox Creates an Unresolved Structural Contradiction

The graph contains two Jevons-related nodes (AI Inference Jevons Paradox, Jevons Paradox AI Inference Demand) that both carry contradicts and inversely_correlates edges toward LLM Token Deflation Race (weights 9, 9, 8). Simultaneously, dozens of other nodes amplify the same Token Deflation Race. The graph does not resolve whether efficiency-driven demand expansion (Jevons) outweighs deflationary price pressure. Both chains are present and weighted comparably.

5. The Highest-Weighted Relationship in the Graph is a Geopolitical One

China Open-Source AI Soft Power Gambit --[drives, w=9.8]--> Global South AI Infrastructure Alignment carries the single highest edge weight in the dataset (9.8). This edge is validated by Qwen-Llama Ecosystem Displacement --[validates, w=8]--> and reinforced by Sovereign AI Open-Source Bootstrap --[amplifies]--> China Open-Source AI Soft Power Gambit. The geopolitical dimension is structurally more tightly coupled than most technical relationships in the graph.

Feedback Loops

Loop 1 — Bilateral Price-Profitability Reinforcement (2 nodes)

Closed Model Profitability Structural Crisis
  --[amplifies, w=8]--> LLM Token Deflation Race
  --[causes, w=9]--> Closed Model Profitability Structural Crisis

A direct reinforcing cycle: profitability pressure drives pricing competition, which further depresses profitability. No external node is required to sustain it. This loop appears self-sustaining absent countervailing forces (Closed Model Enterprise Safety Premium, Agentic Reliability Compounding Problem, Closed Model IP Indemnification Premium each partially offset it).

Loop 2 — Three-Node Commoditization Amplifier

AI Capability Commoditization Cascade
  --[amplifies, w=8]--> Meta Open-Source Commoditization Strategy
  --[amplifies, w=7]--> AI Inference Jevons Paradox
  --[amplifies, w=8]--> AI Capability Commoditization Cascade

A reinforcing loop: commoditization incentivizes Meta to release more, which (via Jevons demand expansion) increases total AI utilization, which accelerates commoditization further. The Jevons node functions as the connector between strategic release decisions and demand-side amplification.

Loop 3 — Safety Governance Balancing Loop

Open-Weight Irreversibility Safety Crisis
  --[triggers, w=9]--> Open-Source Safety Governance Feedback Loop
  --[constrains, w=8]--> Meta Open-Source Commoditization Strategy
  --[amplifies]--> Open-Source AI Safety Defection Problem
  --[enables, w=8.5]--> CBRN Capability Proliferation Irreversibility

This is a balancing (not reinforcing) loop: safety governance attempts to constrain releases, but the releases themselves create the irreversibility problem that governance cannot retroactively address. The loop is also disrupted by Export Controls as Algorithmic Innovation Catalyst --[undermines, w=8]--> Open-Source Safety Governance Feedback Loop, introducing an external shock that prevents equilibrium.

Loop 4 — Research Compulsion Enabling Competitive Disruption

Synthetic Data Closed-to-Open Knowledge Transfer
  --[amplifies, w=7]--> Closed Lab Research Publication Compulsion
  --[enables, w=8.8]--> DeepSeek Efficiency Shock
  --[triggers, w=9]--> Open-Source AI Performance Parity Threshold
  --[triggers, w=9]--> Closed Model Profitability Structural Crisis

Not a closed loop in the strict graph sense, but a sequential chain where closed labs’ publication behavior directly enables their own competitive displacement. The loop is structurally incomplete — there is no explicit edge from Closed Model Profitability Structural Crisis back to Closed Lab Research Publication Compulsion, though competitive pressure would logically create that connection.

Non-Obvious Connections

NVIDIA benefits from model commoditization

NVIDIA Open-Source Infrastructure Paradox --[benefits_from, w=9]--> Meta Open-Source Commoditization Strategy and --[benefits_from, w=8]--> AI Capability Commoditization Cascade. The mechanism is Jevons Paradox AI Compute Loop --[explains, w=9]--> NVIDIA Open-Source Infrastructure Paradox: model commoditization lowers per-inference cost, which expands total inference volume, which increases GPU demand. The company most exposed to model efficiency gains structurally benefits from the commoditization cascade through demand expansion rather than per-unit economics.

Closed labs’ publication compulsion enables competitor capability

Closed Lab Research Publication Compulsion --[enables, w=8.8]--> DeepSeek Efficiency Shock, --[enables, w=8]--> MoE Sparse Activation Efficiency, --[enables, w=7]--> Distillation Capability Diffusion. The publication behavior that closed labs use for talent acquisition and scientific credibility is the same mechanism that transfers architectural knowledge to competitors. The graph represents this as structural, not accidental.

OpenAI’s API format creates interoperability that routes around OpenAI

OpenAI API Format De Facto Standard Lock-In --[enables, w=8.5]--> Multi-Model LLM Routing Architecture. The API standard that created initial network effects around OpenAI now enables routing infrastructure that abstracts away model identity. Multi-Model LLM Routing Architecture --[amplifies, w=8.5]--> Closed API Price Floor Collapse — the standard lowers the API floor for all providers, including OpenAI.

Export controls accelerate the capability they were designed to constrain

Chip Export Controls Efficiency Paradox --[triggers, w=9]--> DeepSeek Algorithmic Efficiency Compression and Export Controls as Algorithmic Innovation Catalyst --[triggers, w=9]--> MoE Sparse Activation Efficiency. Compute constraints forced algorithmic optimization; the resulting MoE and quantization techniques are hardware-agnostic and globally distributable. Semiconductor Export Control Open-Source Rebound --[causes_dilemma_for, w=8]--> NVIDIA Open-Source Infrastructure Paradox captures the resulting strategic bind for both NVIDIA and US policy.

Safety alignment functions as a competitive liability in geopolitical contexts

Alignment Safety Tax --[amplifies, w=8]--> Open-Source AI as Geopolitical Weapon. Safety-aligned models are less deployable in contexts that require unrestricted outputs, which pushes actors toward less-aligned open-source alternatives. Open-Weight Safety Stripping Asymmetry --[amplifies, w=8]--> Alignment Safety Tax reinforces this: the ability to permanently remove safety alignment from open-weight models is itself a geopolitical capability differentiator.

Meta’s advertising business cross-subsidizes its competitors’ infrastructure

Meta Social Media Subsidy Model --[funds, w=8]--> Open-Core AI Business Model --[instantiates, w=8]--> Meta Open-Source Commoditization Strategy. Meta funds open-source AI releases via advertising revenue, which commoditizes AI generally — including for competitors building on Llama. Meta Social Media Subsidy Model --[funds]--> Hugging Face Platform Network Effect extends this: Meta partially subsidizes the platform that distributes all open-weight models, not just its own.

Central Mechanisms

Meta Open-Source Commoditization Strategy (40 connections)

Functions as the graph’s central transmission layer. It is downstream of business model incentives (Meta Social Media Subsidy), technical enablers (Llama ecosystems, LoRA, talent acquisition), and regulatory constraints (EU AI Act, Llama License limitations). It is upstream of competitive disruption, safety defection, geopolitical dynamics, and market stratification. High connection count reflects that it is the operationalization point where many distinct forces converge into a single observable strategic behavior.

Constraints visible in the graph: Llama Commercial License Trap --[constrains, w=8]-->, Open-Source Safety Governance Feedback Loop --[constrains, w=8]-->, Open-Weight Licensing Labyrinth --[undermines, w=8]-->, MoE VRAM Paradox --[constrains, w=7]-->. Four distinct constraint mechanisms operate simultaneously — the strategy is not unchecked.

LLM Token Deflation Race (36 connections)

Functions as a convergence accumulator. Inputs arrive from efficiency gains (Quantization Democratization Cascade, AMD ROCm Open Hardware Insurgency, Hardware Moat Erosion via Open Frameworks), routing infrastructure (AI Gateway Commoditization Flywheel, Multi-Model LLM Routing Architecture, OpenAI API Compatibility Standard), and strategic behavior (Open-Core AI Business Model, NVIDIA Open-Source Hardware Subsidy Strategy, Hyperscaler Open-Source Portfolio Hedge). Its low node weight (w=1) despite high connectivity suggests it was auto-created as an association target rather than explicitly theorized — the graph may understate its structural role.

AI Capability Commoditization Cascade (31 connections)

Structurally distinct from the other hubs: it is bidirectionally connected to both upstream technical events and downstream market consequences. Constrained by three nodes (Agentic Reliability Compounding Problem, Post-Training Alignment Value Stack, Open-Source Safety Governance Feedback Loop, Algorithmic Efficiency Convergence Ceiling), each representing a different class of limit (reliability, alignment, governance, physics). The presence of four distinct constraint mechanisms suggests the cascade is not unbounded in the graph’s model.

Proprietary Data Flywheel Moat (25 connections, w=1)

The most contested node in the graph. Reinforced by: Synthetic Data Self-Training Flywheel, LoRA Fine-Tuning Post-Commoditization Moat, Post-Training Alignment Value Stack, RLHF Preference Data Asymmetry, Fine-Tuning Domain Specialization Moat, Vertical AI Specialization Commoditization Escape. Undermined by: Synthetic Data Moat Erosion Mechanism, Synthetic Data Closed-to-Open Knowledge Transfer, RLHF Alignment Commoditization, Hugging Face Platform Network Effect, LoRA Fine-Tuning Cost Democratization. The graph records no resolution — equal-weight forces point in both directions.

Open-Source AI Performance Parity Threshold (22 connections, w=8)

The only hub node with high explicit weight (w=8). Acts as a structural inflection point: it is triggered by upstream events (DeepSeek Efficiency Shock, Meta strategy, LoRA economics) and unlocks downstream consequences (Closed API Price Floor Collapse, Fine-Tuning Domain Specialization Moat, Enterprise Hybrid AI Portfolio Strategy). Constrained by Test-Time Compute Reasoning Gap --[constrains, w=8]--> and Agentic Reliability Compounding Problem --[undermines, w=7.5]--> — two capability dimensions where parity has not been reached.

Tensions & Open Questions

1. Jevons Paradox vs. Token Deflation (unresolved structural contradiction)

AI Inference Jevons Paradox --[contradicts, w=9]--> LLM Token Deflation Race co-exists with Jevons Paradox AI Inference Demand --[inversely_correlates, w=9]--> LLM Token Deflation Race alongside dozens of amplifies edges targeting the same node. The graph represents both mechanisms as active simultaneously. Whether demand expansion absorbs deflationary pressure depends on the relative rate of efficiency gains vs. new use case discovery — the graph does not model this dynamic.

2. Proprietary Data Moat direction (contested)

As noted above, Proprietary Data Flywheel Moat has roughly equal-weight reinforcing and undermining edges. Synthetic Data Moat Erosion Mechanism --[undermines, w=8]--> operates against Synthetic Data Self-Training Flywheel --[amplifies, w=8]-->. The graph contains the argument that closed labs’ synthetic data builds the moat and the counter-argument that open models’ access to distilled knowledge erodes it — without resolving which dominates.

3. Safety governance sustainability

Export Controls as Algorithmic Innovation Catalyst --[undermines, w=8]--> Open-Source Safety Governance Feedback Loop and Open-Weight Safety Stripping Asymmetry --[undermines, w=9]--> Incumbent Regulatory Capture via Safety Framing. The safety governance mechanism is simultaneously being established (via EU AI Act, CBRN proliferation concerns) and undermined (via geopolitical competition, jailbreak asymmetry). The graph presents no equilibrium point.

4. NVIDIA’s net position

NVIDIA Open-Source Infrastructure Paradox receives benefits_from edges from commoditization AND creates_dilemma_for from Semiconductor Export Control Open-Source Rebound, constrains from Sovereign AI National Independence Trap, and competitive pressure from AMD ROCm Open Hardware Insurgency. The graph records the paradox but does not quantify whether Jevons demand expansion outweighs hardware moat erosion.

5. Mid-Tier AI Lab escape paths

Mid-Tier AI Lab Structural Squeeze (23 connections) receives compression from 15+ distinct mechanisms and has only one explicitly named escape path: Open-Core AI Business Model --[constrains, w=7]-->. Whether this is sufficient is unresolved. The graph also suggests LoRA Fine-Tuning Post-Commoditization Moat --[undermines, w=7]--> Mid-Tier AI Lab Structural Squeeze as a second mitigation, but no outgoing edges from Mid-Tier AI Lab Structural Squeeze indicate successful adaptation.

6. Benchmark Goodhart Collapse implications

Benchmark Goodhart Collapse (w=1) is positioned downstream of Production Evaluation Fragmentation, Agentic Reliability Compounding Problem, Alignment Safety Tax, and Post-Training Alignment Value Stack. Its primary outgoing edge is Post-Parity Operational Differentiation Axes --[emerges_from, w=8]--> it. The graph suggests benchmarks stop being useful at the same time that a new differentiation framework (operational, not capability-based) emerges. Whether enterprises successfully adopt the new differentiation axes is not addressed.

Hypotheses

H1. Token price floor stability

The graph’s Jevons contradiction generates a testable prediction: if Jevons demand expansion dominates, token prices will stabilize at a floor above marginal compute cost rather than approaching zero. If token deflation dominates, prices continue declining toward physical limits. Measurable via API pricing trajectory against FLOP efficiency improvements over 12-24 month windows.

H2. Reasoning gap as the last durable closed-model moat

Test-Time Compute Reasoning Gap --[constrains, w=8]--> Open-Source AI Performance Parity Threshold is the primary structural argument for a remaining closed-model capability advantage. Reasoning Model Open-Source Frontier Collapse --[amplifies, w=8]--> AI Competitive Parity Trap simultaneously argues the gap is closing. The testable question: does open-source reasoning performance on AIME, ARC-AGI, or equivalent benchmarks converge with closed models within 18 months of this graph’s construction?

H3. Agentic reliability as the emergent enterprise moat

Agentic Reliability Compounding Problem --[drives, w=8.5]--> Enterprise AI Portfolio Bifurcation. If reliability compounds in multi-step agentic systems as the graph models, enterprise contract values should show a durable premium for closed frontier models specifically in agentic deployments, while open models dominate single-turn use cases. Testable via enterprise procurement data segmented by use case type.

H4. Global South infrastructure alignment as a leading indicator

The graph’s highest-weighted relationship (China Open-Source AI Soft Power Gambit --[drives, w=9.8]--> Global South AI Infrastructure Alignment) predicts that Qwen/DeepSeek model families will dominate AI infrastructure adoption in Global South government and enterprise contexts faster than Western open-source alternatives. Testable via deployment survey data, procurement records, or API traffic from those regions.

H5. Proprietary data moat collapse threshold

The contested Proprietary Data Flywheel Moat has roughly balanced reinforcing/undermining forces. The specific mechanism Synthetic Data Moat Erosion Mechanism --[amplifies]--> Distillation Capability Diffusion suggests a threshold: once synthetic data quality (distilled from frontier models) reaches parity with human-labeled proprietary data, the moat collapses discontinuously. Testable by tracking open-model performance trained exclusively on synthetic data vs. models trained on licensed human data.

H6. Export control effectiveness inversion

Export Controls as Algorithmic Innovation Catalyst --[triggers, w=9]--> DeepSeek Algorithmic Efficiency Compression and Semiconductor Export Control Open-Source Rebound --[caused_by]--> DeepSeek Efficiency Shock together predict that tighter export controls increase, not decrease, the algorithmic efficiency of restricted actors. Testable: measure parameter-efficiency ratios (performance per FLOP) of Chinese vs. US models before and after successive export control tightening rounds.

H7. Safety governance reversal point

Open-Source Safety Governance Feedback Loop --[constrains]--> Meta Open-Source Commoditization Strategy becomes operative only if governance mechanisms gain enforcement capability before open-weight model releases make regulation practically irrelevant (Open-Weight Irreversibility Safety Crisis). The graph presents these as a race. The reversal point — where proliferation makes governance moot — is not explicitly modeled, making it a testable boundary condition for regulatory effectiveness.