AI vs. Fentanyl Flows from China: A Practical Plan

The fentanyl problem isn’t just a public health emergency anymore—it’s an intelligence, logistics, and financial crime problem that happens to kill Americans at scale. Synthetic opioids have been a primary driver of U.S. overdose deaths since 2013, and by late 2025 the pattern is familiar: when authorities restrict one chemical or route, criminal networks shift to new precursors, new shipping tactics, and new intermediaries.

Washington’s recent approach—using tariffs and negotiated chemical scheduling with Beijing—can help, but it won’t hold on its own. Scheduling chemicals is a speed bump, not a roadblock. The reality is that the most adaptable part of the supply chain sits in the “gray space”: nonscheduled chemicals, dual-use industrial compounds, fragmented online marketplaces, and small shipments that look like normal trade.

Here’s the better frame for national security leaders: treat fentanyl precursor flows like a contested supply chain, then use AI-enabled intelligence analysis, cybersecurity, and targeting disciplines to disrupt it—without pretending there’s a single diplomatic “fix.”

Why fentanyl precursor flows are a national security problem

Answer first: Fentanyl flows from China matter to national security because they exploit legitimate global trade and digital ecosystems, fund transnational criminal organizations, and force U.S. agencies to play defense across borders, networks, and financial rails.

The fentanyl supply chain behaves less like traditional drug trafficking and more like distributed manufacturing plus e-commerce:

• Chemical innovation cycles: When one precursor is scheduled, producers pivot to adjacent compounds and alternate synthesis pathways.
• Low-signature logistics: Precursors move through small parcels, freight consolidators, and re-exports via third countries.
• Digital brokerage: Suppliers, brokers, and buyers connect through online storefronts, encrypted messaging, and gray-market payment channels.

That’s why a policy toolkit focused mainly on “ban the chemical” keeps arriving late. As War on the Rocks highlighted, China’s scheduling of specific fentanyl precursors is meaningful, but chemists can often substitute from broadly used industrial inputs that no government can realistically ban at scale.

In the AI in Defense & National Security context, this is exactly the type of threat that benefits from AI: high-volume data, weak signals, adversarial adaptation, and the need to connect dots across domains.

What Washington has done so far—and what it misses

Answer first: Tariffs and precursor scheduling create leverage and friction, but they don’t solve the central operational challenge: identifying and stopping nonscheduled, dual-use inputs and the networks that move them.

Recent U.S.-China engagement has produced bursts of cooperation, including resumed precursor controls and, after high-level talks in 2025, further restrictions on fentanyl-related exports in exchange for tariff reductions. Those steps can reduce certain “known” flows.

The gap: nonscheduled precursors and legal asymmetry

The most important supply-chain risk sits outside neat regulatory lists:

• Nonscheduled precursors can be lawful industrial chemicals until they’re combined with criminal intent.
• Dual-use legitimacy makes enforcement politically and commercially sensitive.
• Legal-tool mismatch (on any side) can limit the ability to prosecute facilitators, supporting actors, and networks as networks.

This is where Washington should be blunt: the objective isn’t perfect interdiction; it’s sustained disruption that raises cost, increases failure rates, and constrains scale.

The AI-enabled disruption model: target the network, not the molecule

Answer first: The strongest approach is a network-disruption strategy powered by AI—mapping suppliers, brokers, shippers, and money flows—then applying precise enforcement and cyber operations to break the chain repeatedly.

If you’ve worked counterterrorism or counterproliferation, the pattern is familiar. You don’t “ban terrorism.” You degrade networks through intelligence fusion, targeting, and partner operations. Fentanyl is different in motives and volume, but the method translates.

1) Trade analytics that flags risk without stopping commerce

Customs and trade enforcement face an impossible math problem: enormous legitimate trade volume vs. tiny, lethal illicit quantities.

AI helps by shifting from “search everything” to risk scoring:

• Anomaly detection on shipments: unusual routing, packaging patterns, sudden changes in exporter behavior, suspicious commodity descriptions.
• Entity resolution: linking slightly different company names, addresses, and phone numbers across manifests, invoices, and registries.
• Graph analysis: identifying clusters of exporters, freight forwarders, and consignees that repeatedly touch suspicious flows.

Practical stance: Washington should prioritize a national trade-risk model that’s shared (in controlled form) across DHS components, DEA, Treasury, and key allies—so traffickers can’t exploit agency silos.

2) Cyber-enabled intelligence on the brokerage layer

The precursor business runs on communication and marketing—supplier catalogs, broker introductions, payment instructions, shipping promises.

AI-supported cyber and open-source intelligence can:

• Monitor marketplace churn (new storefronts, new product codes, new euphemisms)
• Detect adversarial language patterns in listings (e.g., coded names, “research chemical” claims paired with shipping guarantees)
• Identify infrastructure reuse (domains, hosting, contact handles, messaging IDs)

Done well, this produces a pipeline of targets for law enforcement action, sanctions, or partner takedowns.

3) Financial intelligence that follows small payments at scale

Precursors and services often move through fragmented transactions—payments that look innocuous in isolation.

AI can support:

• Pattern detection across many small transfers
• Typology discovery (new laundering methods, new payment-service abuse)
• Beneficial ownership inference where corporate shells obscure control

This is where Treasury’s tools and national security priorities align. The goal is to remove trusted payment options and force networks into riskier, slower, more failure-prone channels.

A useful rule: if enforcement only hits the shipment, the network learns; if enforcement hits the money and communications, the network struggles to regenerate.

4) Border sensing plus AI triage (and realistic expectations)

Border security matters, but “detect fentanyl everywhere” is a losing proposition. The quantities are too small, concealment is too easy, and screening capacity is finite.

AI works when it’s used to triage and prioritize:

• Fusing nontraditional signals (trade anomalies, prior seizures, entity networks) into inspection queues
• Prioritizing inspection for shipments tied to high-risk entities rather than random sampling
• Rapid feedback loops: every seizure updates the model and refines the next week’s targeting

The win condition is more hits per inspection hour, not a fantasy of total coverage.

Policy moves Washington should make next (that actually scale)

Answer first: Washington should pair diplomacy with an operational plan: modernize legal tools, build an AI-driven fusion architecture, and measure success by disruption metrics—not press releases.

Here are five moves that fit the 2026 reality and the AI in national security playbook.

1) Build a fentanyl “fusion cell” with real authority

Most companies—and plenty of governments—get this wrong by assuming coordination will happen naturally. It won’t.

Create a standing interagency cell that:

• Owns a shared data environment (trade, seizures, cyber tips, financial leads)
• Produces weekly target packages for enforcement and partner action
• Uses a common disruption scoreboard (see below)

If it sounds like counterterrorism fusion, that’s the point.

2) Update legal frameworks to focus on facilitators

The most scalable enforcement targets aren’t always chemists; they’re the enabling layer:

• Brokers who arrange deals
• Freight intermediaries who “solve” routing problems
• Payment processors and laundering services

Washington should prioritize statutes and charging strategies that treat these actors as network participants, not isolated offenders.

3) Make “nonscheduled precursor risk” a diplomatic non-negotiable

Scheduling lists will always lag. U.S. diplomacy with China (and with transit countries) should focus on investigative cooperation and evidence-based actions against suppliers and brokers—especially for nonscheduled but clearly suspicious exports.

That means pushing for:

• Faster information sharing on suspicious exporters
• Joint action on repeat offenders and front companies
• Enforcement against companies that knowingly enable diversion, even if the chemical is lawful

4) Treat shipping and logistics like critical infrastructure for enforcement

A small number of logistics nodes can account for outsized throughput. Washington should work with carriers, freight forwarders, and platforms on:

• Standardized suspicious shipment indicators
• Secure reporting channels that protect commercial data
• Audit regimes for repeat-risk entities

AI helps here by reducing false alarms and focusing compliance teams on the riskiest percent of activity.

5) Measure the right outcomes

If the only metric is “tons seized,” you’ll reward the system for catching the obvious stuff.

Better disruption metrics include:

• Time-to-reconstitution after a takedown (how fast the network rebuilds)
• Price and purity volatility in affected markets (instability is disruption)
• Network fragmentation (fewer high-trust brokers, more failed transactions)
• Repeat-entity interdiction rate (are the same actors still operating?)

These are the metrics that tell you whether AI-enabled intelligence analysis is actually changing adversary behavior.

“People also ask” questions policymakers raise (and the blunt answers)

Can AI detect fentanyl precursors reliably?

AI can reliably detect patterns—entities, routes, behaviors, and anomalies. It won’t magically “sense” every precursor in every box. The operational value is prioritization and network mapping.

Won’t traffickers adapt to AI models?

Yes. That’s why models must be refreshed continuously and paired with human tradecraft. Adaptation is the norm; the point is to make adaptation expensive and error-prone.

Is cooperation with China necessary?

Helpful, not sufficient. Even strong cooperation won’t cover nonscheduled precursors and indirect routing. The U.S. needs a plan that works in partial cooperation scenarios.

Where does cybersecurity fit?

The fentanyl economy is digitally mediated. Disrupting supplier storefronts, broker communications, and laundering services is a cybersecurity and cyber-intelligence mission as much as a counternarcotics mission.

Where this fits in the AI in Defense & National Security series

Fentanyl precursor flows sit at the intersection of intelligence analysis, cyber operations, and supply-chain security—three pillars that keep showing up across national security AI deployments. The same tools used to track sanctions evasion, illicit procurement, or extremist financing can be adapted for counternarcotics.

Washington doesn’t need a single silver-bullet policy. It needs an operating system: shared data, AI-driven targeting, legal tools aimed at facilitators, and disruption metrics that reward real progress.

If you’re building or buying AI for border security, intelligence fusion, or financial crime analytics, the question to ask in 2026 is straightforward: Can your system connect trade, cyber, and finance signals into actions that slow network regeneration? If it can, you’re not just reacting—you’re forcing the market to break down.