AI Strategies to Disrupt Fentanyl Flows from China

A hard truth sits behind the fentanyl crisis: supply chains beat slogans. Even when governments schedule (ban or tightly control) a set of fentanyl precursors, illicit chemists route around the control using nonscheduled chemicals, substitute reagents, gray-market brokers, and fragmented shipping paths that are designed to look ordinary.

That’s why the recent cycle of U.S.-China pressure-and-cooperation—tariffs, retaliation, then partial deal-making—can’t be the whole plan. Diplomatic agreements can reduce specific exports, but the fentanyl economy is adaptive. If Washington wants a durable reduction in fentanyl production, it needs a strategy that targets networks, not just chemicals.

For leaders tracking this through an “AI in Defense & National Security” lens, there’s a clear opening: AI isn’t a magic wand, but it’s the most scalable way to find patterns across billions of legitimate transactions that hide a comparatively tiny stream of illicit activity. The goal is straightforward: make it harder, slower, and riskier for traffickers to source precursors, move product, and get paid.

Why precursor scheduling alone won’t solve fentanyl

Answer first: Scheduling helps, but it’s structurally limited because traffickers can swap inputs and routes faster than governments can update controlled-substance lists.

Policy debates often gravitate toward the cleanest lever: add more precursors to controlled lists. That’s necessary—especially when a chemical has little legitimate use—but it’s not sufficient. As experts have emphasized in the fentanyl policy conversation, criminal chemists can synthesize fentanyl (or fentanyl analogs) from widely used industrial chemicals that governments can’t realistically ban without harming legitimate commerce.

There’s also a second constraint that matters for enforcement: legal frameworks and investigative authorities. If a jurisdiction lacks strong “material support” style provisions (penalizing enabling activity) and tools similar to racketeering or conspiracy statutes, investigators can end up playing whack-a-mole with individual shipments instead of dismantling networks.

A better mental model is counterproliferation. Precursor flows behave like dual-use technology supply chains:

• Most transactions are legitimate.
• The illicit subset is small but catastrophic.
• Bad actors exploit permissive intermediaries.
• Detection requires cross-domain intelligence (trade, financial, cyber, logistics).

That’s an AI-native problem.

Where AI fits in a Washington fentanyl strategy

Answer first: AI provides the “connective tissue” across trade data, shipping telemetry, online marketplaces, and financial signals—turning isolated clues into actionable targeting.

Think about what makes fentanyl supply chains resilient: fragmentation. Brokers, shell companies, relabeling, partial shipments, mixed cargo, and constant substitution. Humans can’t manually spot those patterns at scale. AI can.

In practice, AI enables three advantages that matter in national security operations:

1. Speed: Near-real-time anomaly detection beats quarterly reporting.
2. Coverage: Models can monitor more ports, parcels, vendors, and listings than any team.
3. Network visibility: Graph analytics can identify hidden coordinators and repeat facilitators.

Trade and shipping analytics: finding needles in the logistics haystack

Fentanyl precursors and lab equipment move through container shipping, air cargo, and postal/express parcels. Each channel leaves data exhaust: manifests, bills of lading, customs declarations, routing histories, and scanner metadata.

AI helps by scoring risk across shipments using signals like:

• Unusual routing (detours through known transshipment hubs)
• Frequent small consignments to the same consignee (a classic “smurfing” pattern)
• Mismatched commodity descriptions (e.g., vague “chemical intermediates”)
• Shipper/consignee networks that share addresses, phone numbers, or beneficial owners
• Correlation between chemical purchases and downstream lab equipment procurement

A practical stance I’ve found useful: don’t aim for perfect interdiction; aim for sustained friction. If enforcement can raise the failure rate and cost of sourcing—through targeted seizures, inspections, and compliance actions—criminal groups respond by scaling down, shifting to less efficient methods, or making operational mistakes.

Graph AI to dismantle networks, not just seize shipments

Seizing a shipment is satisfying. It’s also often the least strategic outcome.

Graph AI (network analytics) is built for identifying:

• Brokers coordinating multiple suppliers and buyers
• Logistics facilitators who appear “legit” but serve many suspicious accounts
• Payment hubs laundering proceeds
• Repeat-use freight forwarders and consolidators

The key is fusing multiple data types:

• Corporate registries and beneficial ownership data
• Trade records and consignee histories
• Seizure reports and lab incident data
• Communication metadata (where legally available)
• Financial transaction alerts

This is where national security tradecraft matters: the right output isn’t “a dashboard.” It’s a target package—who, what, where, and how confident we are.

Cyber and online marketplace monitoring: the demand signal is visible

The fentanyl ecosystem isn’t only physical. It’s also digital: chemical vendors advertising “research chemicals,” brokers negotiating in encrypted channels, and retail distribution facilitated online.

AI can support:

• Multilingual OSINT and dark web monitoring to detect new vendor storefronts, aliases, and product phrasing
• Entity resolution to connect usernames, wallet addresses, emails, and shipping patterns
• NLP-based clustering to identify listings that are functionally the same product with slightly altered names
• Malicious infrastructure mapping to link domains, hosting, and traffic patterns

The win isn’t just taking down a listing. It’s using online data to feed real-world targeting: shipments, money flows, and the facilitators who make the business repeatable.

A “fentanyl intelligence stack” Washington can actually deploy

Answer first: Treat fentanyl as a national security intelligence mission with a modern data stack—fusion, scoring, targeting, and feedback loops.

Agencies already have many ingredients, but they’re often siloed by mission, legal authority, or technology. A workable architecture looks like this:

1) Data fusion that respects civil liberties (and still works)

Washington doesn’t need mass surveillance to build effective models. It needs purpose-built, auditable pipelines with clear minimization and governance.

Core inputs typically include:

• Customs and shipment data (container, air cargo, express parcels)
• Chemical import/export licensing and compliance records
• Seizure and lab incident data (structured and standardized)
• Financial intelligence alerts (aggregated patterns, not raw consumer data)
• Open-source vendor and marketplace signals

Design principle: log every query, version every model, and preserve the chain of reasoning. If you can’t explain why a shipment was flagged, you’ll lose in court, in oversight, or both.

2) Risk scoring that drives action, not noise

Models should output ranked priorities tied to specific action options:

• Inspect / hold / sample testing
• Compliance audit of shipper or forwarder
• Controlled delivery (where appropriate)
• Financial investigation trigger
• Diplomatic demarche supported by evidence

A common failure mode is “alert fatigue.” The fix is to measure model value the way operators do:

• Precision at the top (How many of the top 50 leads are real?)
• Time-to-interdict (How quickly can a team act?)
• Network impact (Did arrests/seizures remove a facilitator node?)

3) Feedback loops: models that learn from outcomes

Interdiction data is messy, but it’s gold. Each inspection outcome, seizure, prosecution, and false positive should feed back into model training.

The goal is a living system that adapts at least as fast as traffickers do.

A useful standard: if your model updates slower than trafficking tactics change, you’re funding a history project.

Policy moves that make AI enforcement more effective

Answer first: AI works best when law, diplomacy, and enforcement incentives line up—otherwise the models just produce insights nobody can act on.

The source article highlights the limits of focusing only on scheduled chemicals and points to legal and enforcement gaps. Washington can translate that into a set of practical moves that amplify AI’s value.

Strengthen evidence pathways for international cooperation

AI-based targeting often produces probabilistic assessments. To make those usable in partner engagements, agencies need standardized evidentiary packets:

• Clear description of suspicious behavior patterns
• Corroboration across independent data sources
• A short list of high-confidence entities (companies, brokers, individuals)

This is how you turn “we think something is happening” into “here are the top 12 facilitators, the shipment patterns, and the bank/payment rails they rely on.”

Focus on enablers: freight, finance, and platforms

If Washington keeps treating fentanyl like a pure narcotics problem, it will keep getting narcotics outcomes. The scalable pressure points are enablers:

• Freight forwarders and consolidators that repeatedly touch suspect shipments
• Payment processors, OTC crypto brokers, and mule networks
• Chemical marketplaces and B2B vendor ecosystems that look the other way

AI helps identify these repeat enablers across cases that look unrelated.

Build a “nonscheduled precursor watchlist” process

Scheduling is slow; substitution is fast. Washington should operationalize a watchlist approach for nonscheduled precursors and substitute pathways:

• Rapid analytic review of emerging chemicals used in synthesis routes
• Voluntary compliance advisories to legitimate manufacturers and shippers
• Targeted inspections and sampling for high-risk combinations

This doesn’t require banning the chemicals. It requires making suspicious patterns visible.

What leaders in defense and national security should ask next

Answer first: The most productive questions are operational: What data do we have, what actions can we take, and how do we measure disruption?

If you oversee an intelligence, defense, or interagency mission set, here are the questions worth putting on the agenda:

1. What’s our fastest path to multi-agency data fusion for fentanyl precursor tracking?
2. Do we have graph analytics to identify facilitators across cases, or are we stuck in case-by-case mode?
3. Can we connect cyber marketplace monitoring to physical interdiction in weeks, not months?
4. How do we evaluate success—pure seizure volume, or network degradation and reduced availability?
5. Are our models auditable enough to support prosecutions and oversight?

These questions keep AI grounded in outcomes rather than procurement theater.

The stance Washington should take in 2026

Diplomacy with China on fentanyl precursors matters, and scheduling specific chemicals is still worth doing. But the center of gravity is the adaptive, nonscheduled ecosystem—the brokers and facilitators who thrive on complexity.

AI gives Washington a way to compete in that complexity. Not by “predicting crime” in the abstract, but by connecting trade, cyber, financial, and logistics signals into targetable intelligence that raises the cost of doing business for trafficking networks.

If you’re building capabilities in the AI in Defense & National Security space, this is one of the clearest missions where AI can produce measurable impact: fewer successful shipments, more disrupted networks, and faster learning as traffickers change tactics. The open question is whether Washington will treat fentanyl as a sustained national security intelligence problem—or keep hoping the next list of banned chemicals will do the job.