Unmanned and Unprepared:

The Navy's Mine Warfare Gamble at Hormuz

Feature • Mine Warfare

April 2026

The U.S. Navy bet its mine countermeasures future on unmanned platforms. The Strait of Hormuz is now the first real-world test of that wager—and the early returns are sobering.

By Stephen L Pendergast, Lt USNR, Retired Senior Engineer Scientist; IEEE Senior Life Member

Bottom Line Up Front

The U.S. Navy's mine countermeasures force facing Iran's mined Strait of Hormuz in April 2026 is built almost entirely around unmanned platforms that have never been validated in a contested operational environment. The service retired its last purpose-built Avenger-class minesweepers from the Persian Gulf in September 2025 and shut down its MH-53E Sea Dragon airborne MCM detachment in August 2025, replacing both with an LCS-based mission package whose unmanned surface vehicles, unmanned underwater vehicles, and helicopter-borne sensors have been documented by the Navy's own training assessments as suffering from unreliable sonar recording, excessive pre-mission maintenance timelines, critical single-point mechanical failures, and inadequate sensor performance in turbid or deep water. Two legacy Avengers are being surged from Japan, but the force structure now confronting Iranian Maham-3 moored influence mines and Maham-7 bottom mines consists principally of three Independence-class LCS, 16 Expeditionary MCM companies operating Mk 18 Mod 2 Kingfish UUVs, a handful of Knifefish medium-class UUVs that have never operated outside controlled test environments, and Raytheon's Barracuda mine neutralizer—which completed its first untethered autonomous demonstration only nine months ago. The gap between the Navy's unmanned MCM ambitions and the reality of clearing a 100-nautical-mile strait against a dynamic, sensor-fused mine threat represents the most consequential mine warfare test since the 1991 Kuwait clearance operation.

The Operational Problem

On 11 April 2026, USS Frank E. Petersen Jr. (DDG-121) and USS Michael Murphy (DDG-112) became the first U.S. warships to transit the Strait of Hormuz since Operation Epic Fury began on 28 February, opening what U.S. Central Command described as the first phase of mine clearance operations. CENTCOM Commander Admiral Brad Cooper announced that forces would establish “a new passage” and share a verified safe corridor with commercial shipping. Additional forces, explicitly including underwater drones, would join the clearance effort within days.

The scope of the problem is staggering. The strait’s traffic separation scheme stretches roughly 100 nautical miles. As Captain Kevin Eyer, USN (Ret.), detailed in the April 2026 Proceedings, supporting both inbound and outbound oil and LNG traffic would require two Q-routes, each 2,000 yards wide, covering approximately 200 square miles of seabed that must be surveyed, classified, and declared safe. Before the war, the strait handled roughly 130 ships per day—approximately 20 percent of global seaborne oil and a comparable share of liquefied natural gas. In the first week of the blockade, fewer than a dozen ships transited without IRGC authorization.

U.S. intelligence, as reported by CBS News on 23 March 2026, estimated that Iran had deployed at least a dozen naval mines in the strait. These were identified as two types: the Maham-3, a moored influence mine weighing approximately 300 kilograms equipped with magnetic and acoustic sensors capable of engaging targets within about 10 feet; and the Maham-7, a 220-kilogram seabed bottom mine whose hull geometry was engineered to scatter incoming sonar waves, significantly complicating detection. According to the CAT-UXO (Collective Awareness to Unexploded Ordnance) database, the Maham-7 can be deployed by small craft or helicopters in water as shallow as 10 feet or as deep as 300 feet. The Defense Intelligence Agency estimated Iran’s total mine stockpile at more than 5,000 as of 2019, and CENTCOM assessments indicate Iran retains 80 to 90 percent of its small-boat and minelayer capacity.

Critically, reporting from multiple intelligence sources indicates that Iran itself may not have systematically tracked every mine placement—a product of IRGC Navy units operating fast attack boats capable of carrying two or three mines per sortie under combat conditions. This creates a problem qualitatively different from the 1991 Kuwait precedent: the minefield is not static. The IRGC Navy declared on 5 April that the strait would “never return to its previous status,” and evidence indicates mining may have continued even during the ceasefire.

What the Navy Retired

To understand the current MCM force structure, one must first understand what the Navy gave up. On 25 September 2025, the service held a final decommissioning ceremony in Bahrain for the last four forward-deployed Avenger-class MCM ships: USS Devastator (MCM-6), USS Sentry (MCM-3), USS Dextrous (MCM-13), and USS Gladiator (MCM-11). In January 2026—five weeks before Iran reportedly began mining the strait—those hulls were loaded aboard the heavy-lift vessel M/V Seaway Hawk and transported to Philadelphia for disposal.

The Avenger class represented 40 years of purpose-built mine warfare engineering. Their fiberglass-sheathed wooden hulls were specifically designed for minimal magnetic signature, enabling them to operate inside minefields where steel-hulled ships cannot safely go. They carried the AN/SQQ-32 mine-hunting sonar and the AN/SLQ-48 Mine Neutralization System, and their crews trained exclusively for mine warfare. The Navy has stated it has no plans to recommission any Avenger-class vessels.

Concurrently, the Navy’s MH-53E Sea Dragon detachment in the Arabian Gulf—the backbone of airborne MCM operations in the Fifth Fleet area for decades—was shut down in August 2025 as the platform was phased out. The service no longer maintains a dedicated heavy-lift airborne mine countermeasures capability in the region.

Four Avenger-class ships remain in Navy inventory, all forward-deployed to Sasebo, Japan. Two of those—USS Pioneer (MCM-9) and USS Chief (MCM-14)—were spotted departing Singapore westbound on 10–11 April, transiting the Strait of Malacca en route to CENTCOM. Their transit time to the Persian Gulf will be measured in weeks, not days.

The Unmanned Architecture

The replacement force structure rests on three pillars, each relying heavily on unmanned systems: the LCS MCM Mission Package, the Expeditionary MCM companies, and a developmental pipeline of next-generation unmanned vehicles. Each warrants detailed examination.

Pillar 1: The LCS MCM Mission Package

Three Independence-class LCS currently constitute the Navy’s primary MCM surface force in the region: USS Canberra (LCS-30), USS Tulsa (LCS-16), and USS Santa Barbara (LCS-32). Each carries the MCM Mission Package (MCM MP), which achieved Initial Operational Capability on 31 March 2023 aboard USS Cincinnati (LCS-20). The first operational packages deployed from San Diego in March 2025 aboard Canberra and Santa Barbara.

The MCM MP is a layered system designed to keep the manned ship outside the mine danger area while unmanned and airborne systems operate forward. Its principal components and vendors are:

MCM Mission Package: Key Systems and Vendors

MH-60S Seahawk	Mine warfare helicopter; carries ALMDS and AMNS (Sikorsky/Lockheed Martin)
AN/AES-1 ALMDS	Airborne Laser Mine Detection System. Pulsed laser and streak tube imaging LIDAR detects, classifies, and geolocates floating and near-surface moored mines. Day/night, untethered operations. (Northrop Grumman, Melbourne, FL)
AN/ASQ-235 AMNS	Airborne Mine Neutralization System. Helicopter-deployed, expendable, remotely operated mine neutralizer with onboard camera and explosive warhead. (Raytheon / RTX)
MCM USV (Fleet-class)	Common Unmanned Surface Vehicle, 11-meter platform. Tows sonar or sweep systems. Originally developed by Textron Systems; production transferred to Bollinger Shipyards (Lockport, LA) in April 2022.
AN/AQS-20C Sonar	Forward-look and side-scan sonar towed by USV for volume and bottom mine hunting. (Raytheon / RTX)
UISS	Unmanned Influence Sweep System. Towed by USV; reproduces magnetic and acoustic ship signatures to trigger influence mines. Minesweep Payload Delivery System by Textron Systems.
Knifefish UUV	Medium-class MCM UUV. Low-frequency broadband synthetic aperture sonar for buried, bottom, and volume mine detection. Based on Bluefin-21 AUV. 21-inch diameter, ~1,650 lbs, up to 25 hours endurance. (General Dynamics Mission Systems, Taunton, MA / Quincy, MA)
AN/WSQ-46 Barracuda	Expendable, semi-autonomous mine neutralization vehicle. Sonobuoy-sized (~3 ft long, 5-inch diameter). Navigates to mine location and detonates. First untethered autonomous demo July 2025. (Raytheon / RTX, Portsmouth, RI)

The concept is elegant in theory: the ALMDS-equipped MH-60S sweeps the near-surface volume with laser imaging; the USV-towed AN/AQS-20C sonar hunts mines in the water column and on the bottom; the Knifefish UUV autonomously searches for buried mines in high-clutter environments; the UISS triggers influence mines; and the Barracuda neutralizes confirmed contacts. The LCS itself never enters the minefield.

The problem lies in execution. A Navy MCM Advanced Tactical Training brief—the final pre-deployment mine warfare assessment for LCS crews—documented systemic deficiencies that were reported by Hunterbrook Media in March 2026. Each Fleet-class USV mission required more than four hours of pre-mission maintenance followed by one and a half hours of GPS and sonar calibration. Multiple hunt missions were conducted where the AN/AQS-20 sonar failed to record data, a failure discoverable only during post-mission analysis. The USV exhibited a recurring tendency to “run away” beyond operator control, and its communications range required the LCS mothership to operate dangerously close to, or inside, the minefield—precisely the scenario the standoff architecture was designed to prevent.

Single points of failure compound the reliability issues. The platform lift moving equipment from the LCS mission bay to the flight deck is a critical node; its failure renders the helicopter combat-ineffective. If the USV tow hook breaks, the vehicle must be recovered by other means. If the Twin Boom Extensible Frame used to launch and recover USVs from the mission bay fails, the entire MCM platform is inoperable. Captain Scott B. Hattaway, Director of the SMWDC Mine Countermeasures Technical Division, acknowledged at the Combined Naval Event 2024 in the UK that the 11-meter USV’s form factor limits both the endurance and the towed sonar depth achievable by the AN/AQS-20C.

Navy doctrine further requires visual identification of mines before neutralization. The camera system on the USV reportedly fails even in relatively clear water, a significant limitation in the turbid conditions common in the Persian Gulf and Strait of Hormuz.

Pillar 2: Expeditionary MCM Companies

The Navy’s Explosive Ordnance Disposal community maintains 16 Expeditionary MCM (ExMCM) companies—27-person units composed of a command element, an unmanned systems platoon, an EOD MCM platoon, and a post-mission analysis cell. These companies have deployed extensively in the Middle East, with two continuously forward-deployed to Bahrain since 2014, and represent arguably the Navy’s most operationally proven MCM capability.

Each unmanned systems platoon operates 12 UUVs: six Mk 18 Mod 1 Swordfish (based on the Hydroid REMUS 100, approximately 80 pounds, 6–8 hour endurance, 100-meter max depth) and six Mk 18 Mod 2 Kingfish (based on the REMUS 600, approximately 800 pounds, 20–24 hour endurance, 600-meter max depth). Production of the Mk 18 Mod 2 was completed in 2023 after Hydroid, now a Huntington Ingalls Industries subsidiary acquired for $350 million in 2020, delivered more than 90 vehicles to the fleet.

The Kingfish uses side-scan sonar for search and discovery, Iridium satellite communications for over-the-horizon connectivity, and an autonomous navigation suite combining acoustic Doppler current profiler, inertial navigation, and P-code GPS. Crews launch and recover the UUVs from 11-meter rigid-hull inflatable boats or, in a field innovation developed by ExMCM operators in Bahrain, from a purpose-engineered rubber raft called the “Mallard” towed behind a Zodiac combat rubber raiding craft. The system can also deploy from a containerized “Stinger” launcher fitting a standard MilVan, enabling operation from virtually any vessel of opportunity.

The ExMCM companies are the Navy’s most flexible MCM asset—deployable by C-130 transport aircraft, operable from any port or platform, and staffed by EOD technicians who can visually identify and manually neutralize mines when unmanned systems reach their limits. Their principal limitation is throughput: clearing 200 square miles of seabed with RHIB-launched UUVs is a fundamentally slower process than operating from dedicated MCM ships.

Pillar 3: The Developmental Pipeline

Two significant unmanned systems are in development but are not yet fielded at scale for the Hormuz mission:

Knifefish Block 1 (General Dynamics Mission Systems). The Knifefish is the MCM MP’s dedicated subsurface mine-hunting UUV, purpose-built to detect buried and bottom mines in high-clutter environments using a low-frequency broadband synthetic aperture sonar with automated target-recognition software. Built on the Bluefin-21 platform, it is 21 inches in diameter, approximately 16 feet long, and weighs roughly 1,650 pounds. NAVSEA awarded General Dynamics a $44.6 million LRIP contract in 2019 after Milestone C approval, followed by a $72.8 million retrofit contract to upgrade five Block 0 systems to Block 1 configuration for deeper-depth operation and improved sensor performance. The Navy plans to procure 30 Knifefish systems (48 UUVs total)—24 for LCS and six for other vessels. However, as one analysis of the Hormuz deployment noted, the Knifefish’s April 2026 employment would represent its first use in a contested operational environment; whether its sonar can reliably classify a Maham-7 bottom mine against the acoustic clutter of the Hormuz seabed has not been validated outside controlled test settings.

Viperfish / Medium UUV (Leidos). The Viperfish is the designated successor to the Mk 18 Mod 2 Kingfish for ExMCM operations. In July 2022, the Navy awarded Leidos a $12 million design contract for the Medium Unmanned Undersea Vehicle, with options potentially reaching $358.5 million through 2032. In July 2023, Leidos received a $36.3 million contract modification to fabricate four engineering development models. Based on the L3Harris Iver4 900 UUV, Viperfish will combine mine countermeasures and submarine-based oceanographic sensing in a single modular platform, merging the requirements of the EOD community’s Kingfish replacement and the submarine community’s Razorback program. Leidos has described Viperfish as potentially “one of the most densely packed and technologically advanced underwater vehicles ever built,” but it remains in development and is not available for the current Hormuz operation.

Barracuda (Raytheon / RTX). The AN/WSQ-46 Barracuda mine neutralization vehicle represents the endgame of the unmanned MCM kill chain—an expendable, semi-autonomous UUV roughly the size of a sonobuoy that autonomously navigates to a mine contact, identifies it with onboard sensors, and detonates its warhead to destroy it. Raytheon won the initial $83.3 million design contract in April 2018, with options to $362.7 million. In July 2025, Raytheon successfully demonstrated Barracuda in its first untethered, semi-autonomous open-water operation in Narragansett Bay. The updated Barracuda will operate without a tether—a critical improvement over the original tethered concept. Its integration into the MCM MP is ongoing, with the system designed to be deployed from the Fleet-class USV.

The Force Protection Problem

Mine clearance is not merely a technical task; it is a combined-arms problem. MCM forces are inherently vulnerable because they must move slowly and methodically, focused on the water column and seabed, while exposed to aviation, drone, missile, and small-boat threats. The Strait of Hormuz places every MCM asset within range of Iran’s surviving arsenal of antiship cruise missiles, Shahed one-way attack drones, fast-attack boats, and shore-based launchers.

Admiral Daryl Caudle, the Chief of Naval Operations, addressed this directly in the April 2026 Proceedings: mine search and destruction is slow, deliberate work, and none of the Navy’s current MCM options performs well in a non-permissive environment. This reality explains CENTCOM’s decision to send two Arleigh Burke-class destroyers through the strait as the opening move—not to hunt mines, for which they carry no specialized equipment, but to establish local sea control and provide air and missile defense coverage for the MCM forces that will follow.

The USS George H.W. Bush (CVN-77) Carrier Strike Group, diverted around the Cape of Good Hope rather than risk the Red Sea Houthi threat, is transiting toward the theater. A-10 Warthogs have been conducting close air support operations over the strait, a choice that suggests CENTCOM judges the airspace sufficiently permissive for slow, non-stealthy aircraft—a prerequisite for MH-60S mine warfare helicopter operations.

Historical Context and the Scale of the Challenge

The 1991 Kuwait mine clearance operation is the closest historical benchmark. U.S. and coalition forces swept approximately 200 square miles of shallow water in 51 days—with a full squadron of Avenger-class ships, uncontested airspace, an enemy that had stopped mining weeks earlier, and precisely mapped mine locations. None of those conditions apply to Hormuz in April 2026.

The 1987–88 Tanker War offers a more cautionary parallel. During the first Earnest Will convoy on 24 July 1987, the reflagged tanker Bridgeton struck an Iranian M-08 moored mine immediately after transiting the Strait of Hormuz. No pre-cleared Q-routes existed; escort ships followed in Bridgeton’s wake, using the tanker itself as an improvised minesweeper. Nine months later, USS Samuel B. Roberts (FFG-58) struck another Iranian mine on 14 April 1988, suffering a 15-foot hull breach that broke the ship’s keel—triggering Operation Praying Mantis, the largest U.S. naval surface engagement since World War II.

The Washington Institute has estimated that clearing the Strait of Hormuz could require up to 16 MCM vessels. The Navy has seven—three LCS with MCM packages of unproven reliability and four Avengers, two of which are weeks away from the theater.

The Coverage-Rate Arithmetic

The mismatch between the area to be cleared and the available clearance capacity is the central quantitative fact of this operation. Captain Eyer’s Proceedings analysis established the requirement: supporting two-way oil and LNG traffic through the strait’s 100-nautical-mile traffic separation scheme demands two Q-routes, each 2,000 yards wide, totaling approximately 200 square nautical miles of seabed that must be surveyed, classified, and verified mine-free. CENTCOM’s initial objective is narrower—a single safe corridor—but even a minimum-width lane through the full length of the strait represents an enormous survey task.

The coverage rates of the available systems are sobering. A dedicated mine countermeasures vessel sweeps approximately 0.5 square nautical miles per day under favorable conditions. The Knifefish UUV can operate autonomously for approximately 16 hours, but after subtracting transit time to and from the search area, practical search time per sortie is roughly 10 to 12 hours—and each LCS carries only two Knifefish. The vehicle does not cover large minefields efficiently; Wikipedia’s sourced assessment of the program notes that the Knifefish lacks the endurance to handle large-area search missions, and the Navy has acknowledged this as a limitation relative to the earlier (and canceled) Remote Multi-Mission Vehicle. The Fleet-class USV towing the AN/AQS-20C sonar requires approximately six hours of pre-mission preparation per sortie, and the Navy’s own training data shows sonar data-recording failures that are not discoverable until post-mission analysis—meaning entire sorties can yield zero usable data. The mine clearance rate for the full detect-classify-identify-neutralize kill chain has been estimated at approximately one mine per hour or less in training conditions.

The ALMDS on the MH-60S is the fastest asset in the package—its untethered, forward-motion laser imaging can achieve high area search rates for near-surface moored mines, day or night. But the ALMDS cannot detect bottom mines, and the Maham-7 is specifically a seabed weapon engineered to scatter incoming sonar returns. In the turbid waters common in the Persian Gulf and Strait of Hormuz, ALMDS effectiveness degrades further. The aerial component is also limited to shallow waters no greater than approximately 40 feet, per the Stimson Center’s analysis.

Paul Heslop of the UN Mine Action Service framed the operational reality in an April 2026 interview: mine clearance in the strait would likely require forming convoys behind active sweep operations through a corridor only a few kilometers wide, with continuous re-sweeping required because currents, tidal shifts, and—critically—potential re-mining can re-contaminate previously cleared areas. This is not a one-pass operation.

For context: the 1991 Kuwait clearance of roughly 200 square miles, with a full Avenger squadron, uncontested airspace, a static minefield, and known mine locations, took 51 days. With three LCS of documented unreliability, ExMCM companies operating Mk 18 UUVs from RHIBs, and two Avengers still weeks from theater, clearing 200 square miles is a multi-month proposition under best-case assumptions. Even CENTCOM’s more modest objective of a single verified safe corridor likely requires weeks of sustained operations—assuming the threat environment permits uninterrupted work and no additional mines are laid.

Has Iran’s Mining Capability Been Destroyed?

The answer is: partially degraded, but not eliminated—and the claims emanating from Washington diverge sharply from the available evidence.

On 10 March, CENTCOM announced the destruction of multiple Iranian naval vessels including 16 minelayers in a single day’s strikes near the strait. CENTCOM Commander Admiral Brad Cooper’s update confirmed strikes on more than 5,500 targets in Iran, including over 60 ships and all four Soleimani-class warships. President Trump subsequently claimed on Truth Social that “all 28” of Iran’s mine-laying boats had been sunk. A White House spokesperson stated the Department of War had destroyed over 40 minelaying vessels. Trump later told reporters on 12 April that all of Iran’s mine-laying ships had been destroyed and that only “a couple of mines” remained in the water.

These claims are difficult to reconcile with reporting from multiple credible sources. CNN reported in early March—after the initial wave of CENTCOM strikes—that Iran still retained 80 to 90 percent of its small boats and minelayers, and could feasibly lay hundreds of additional mines. The Stimson Center’s April 2026 analysis emphasized that Iran’s mine warfare doctrine deliberately disperses capability across numerous fast attack boats (each carrying two to three mines per sortie), frogmen who place mines manually from small craft, helicopters, midget submarines, and shore-based rocket artillery capable of delivering mines. This layered architecture was designed precisely to survive attempts to destroy the minelaying force in a single stroke. Destroying dedicated minelayer hulls, while necessary, does not eliminate the capability.

The operational evidence supports the more cautious assessment. The New York Times reported on 10 April—a full month after the claimed destruction of Iranian minelayers—that Iran cannot locate all the mines it has already placed and lacks the capability to remove them. Mining may have continued during the ceasefire itself; the IRGC Navy declared on 5 April that the strait would “never return to its previous status,” a direct contradiction of ceasefire terms that required the strait to reopen. Al Jazeera noted that while CENTCOM confirmed striking 16 vessels in the initial wave, the destruction of the entire mine-laying fleet remains unverified by independent sources.

Iran’s total mine stockpile was estimated by the Defense Intelligence Agency at more than 5,000 as of 2019. Even if CENTCOM destroyed every dedicated minelayer hull afloat, the mines themselves—stored in shore depots, aboard surviving small craft, and available for deployment by frogmen operating from civilian dhows or fishing boats—represent an enduring threat. Admiral Caudle’s observation in Proceedings that none of the Navy’s MCM options performs well in a non-permissive environment takes on particular weight here: clearing mines while the adversary retains even a diminished capacity to lay new ones converts the operation from a clearance problem into an attrition problem. If the IRGC can seed new mines overnight faster than the Navy can find and neutralize them during the day, the operation reaches stalemate regardless of the technology employed.

The Vendor Landscape

Principal Contractors: Navy Unmanned MCM Systems

General Dynamics Mission Systems	Knifefish UUV (SMCM); Bluefin Robotics UUVs. Taunton & Quincy, MA.
Huntington Ingalls Industries (HII) / Hydroid	Mk 18 Mod 1 Swordfish and Mk 18 Mod 2 Kingfish UUVs (REMUS 100/600 platforms). Pocasset, MA. 90+ Mk 18 Mod 2 delivered.
Raytheon / RTX	AN/AQS-20C towed sonar; AN/ASQ-235 AMNS; AN/WSQ-46 Barracuda mine neutralizer. Portsmouth, RI & Tucson, AZ.
Northrop Grumman	AN/AES-1 ALMDS airborne laser mine detection system. Melbourne, FL. 24 pods delivered to USN; 4 to Japan MSDF; 1 to Republic of Korea.
Textron Systems	Fleet-class USV (original developer); Minesweep Payload Delivery System / UISS. Production of USV hulls transferred to Bollinger Shipyards.
Bollinger Shipyards	MCM USV production (from April 2022). Lockport, LA. $7.7M initial AMO contract.
Leidos	Viperfish Medium UUV (in development). $12M design + $36.3M fabrication contracts. Partnered with L3Harris Technologies.
L3Harris Technologies	Iver4 900 UUV platform (basis for Viperfish MUUV); partner on MUUV development.
Sikorsky / Lockheed Martin	MH-60S Seahawk helicopter (MCM mission platform).

The Recurring Institutional Failure

The pattern is over a century old, and its repetition borders on institutional pathology. Western navies consistently allow mine warfare capability to atrophy in peacetime, then face catastrophic consequences when mines appear in combat.

The archetype is the Dardanelles, March 1915. The Royal Navy attempted to force the strait with a combined Anglo-French fleet of 18 capital ships. This despite a prewar study indicating just how hard this would likely be. The Turks had laid 393 mines in ten rows; a line of 20 mines laid secretly by the minelayer Nusret on 8 March, parallel to the Asian shore batteries, went undetected by British minesweeping forces. On 18 March, those mines sank three battleships—HMS Irresistible, HMS Ocean, and the French Bouvet—and crippled three more. The minesweeping force consisted of civilian-manned North Sea trawlers operating at one to four knots under fire, wholly inadequate for the task. The naval failure compelled the Gallipoli land campaign, which cost over 250,000 Allied casualties and achieved nothing. As the Australian Naval Institute noted in March 2026, the entire amphibious campaign on the Gallipoli Peninsula was the unintended consequence of the inability of western navies to clear mines.

Thirty-five years later, the lesson repeated at Wonsan. In October 1950, North Korea—a nation without a navy—used Soviet-supplied mines, loaded onto wooden barges and sampans at the direction of Soviet technical advisors, to lay approximately 3,000 contact and influence mines across 400 square miles of Wonsan’s approaches. The Pacific Fleet no longer had a mine warfare-type commander on its staff. Six minesweepers were assigned to a task that had employed over 100 at Okinawa and 300 at Normandy. On 12 October, the minesweepers USS Pirate (AM-275) and USS Pledge (AM-277) were sunk by mines, with 12 killed. The planned five-day clearance became a 15-day ordeal, delaying D-Day by ten days, during which 50,000 Marines sat aboard transports while South Korean forces captured Wonsan overland, rendering the amphibious assault moot. Rear Admiral Allen “Hoke” Smith sent the Chief of Naval Operations a message that reverberates to this day: “The U.S. Navy has lost control of the seas in Korean waters to a nation without a Navy, using pre–World War I weapons, laid by vessels that were utilized at the time of the birth of Christ.”

The 1991 Gulf War added another verse. USS Tripoli (LPH-10) and USS Princeton (CG-59) both struck Iraqi mines within hours of each other on 18 February, and the planned 17,000-Marine amphibious assault on the Kuwaiti coast was canceled because the mine threat could not be resolved in time. After the war, the Navy accelerated MCM investment—then allowed it to erode once more as the Global War on Terror consumed attention and budget.

The institutional dynamic is well understood. Mine warfare is defensive, unglamorous, and technically demanding. Few admirals come from the MCM community. The platforms are small, the budgets modest, and the mission invisible until it is suddenly the only thing that matters. A Proceedings article in January 2022 drew the Dardanelles parallel explicitly to the Taiwan scenario, noting that China’s PLAN inventory includes an estimated 100,000 naval mines. The lesson is stark: mine warfare capability cannot be built after the mines are in the water.

The Commercial Unmanned Fleet: An Untapped Reserve

If the Navy’s organic MCM capacity is insufficient for the Hormuz mission—and the arithmetic strongly suggests it is—then the question becomes whether the commercial offshore industry possesses unmanned platforms that could supplement the fleet. The answer is yes, emphatically, though the integration challenges are nontrivial. This can't be left to unprepared and unprotected fishermen as it was at the Dardanelles.

The global offshore oil and gas, subsea cable, and offshore wind industries operate thousands of remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs) for pipeline inspection, seabed survey, and subsea infrastructure maintenance. These vehicles carry the same classes of sensors—side-scan sonar, synthetic aperture sonar, multibeam echo sounders, magnetometers, sub-bottom profilers, and high-definition cameras—that the Navy uses for mine hunting. The key difference is scale: the commercial fleet is vastly larger than the military one, and its operational tempo is measured in thousands of survey-days per year.

Several commercial platforms and operators warrant specific attention:

Kongsberg HUGIN AUV Family (Kongsberg Discovery, Horten, Norway). The HUGIN is the most commercially successful AUV in the world. The HUGIN Superior variant carries the HISAS 1032 dual-receiver synthetic aperture sonar—the same sonar technology class that the Belgian-Dutch rMCM program uses for mine detection—alongside a multibeam echosounder, sub-bottom profiler, magnetometer, and camera system. It operates to 6,000 meters depth, navigates via Kongsberg’s Sunstone aided inertial navigation system with DPCA micro-navigated SAS, and communicates via Iridium satellite. The HUGIN Endurance variant, 11 meters long and weighing approximately 8,000 kilograms, is designed for unsupervised shore-to-shore operations with a 15-day mission time and 1,200-nautical-mile range. Its sensor suite is directly applicable to mine detection: the HISAS synthetic aperture sonar produces centimeter-resolution imagery of the seabed, and its magnetometer can detect ferrous objects buried in sediment. Kongsberg has explicitly marketed HUGIN for both commercial survey and defense MCM applications, and the vehicle is used by the Royal Norwegian Navy for mine reconnaissance.

Ocean Infinity (Austin, Texas / Southampton, UK). Ocean Infinity operates the Armada fleet—14 robotics-equipped survey vessels ranging from 78 to 86 meters, completed in December 2025, plus 14 Kongsberg HUGIN AUVs, eight unmanned surface vehicles, and six work-class ROVs. The Armada vessels are designed for remote operation from shore-based control centers via satellite, with minimal or zero crew aboard. They carry permanently mounted multibeam echosounders, sub-bottom profilers, USBL positioning systems, and can deploy multiple AUVs simultaneously from moonpools. During the 2018 search for Malaysia Airlines Flight 370, Ocean Infinity’s multi-AUV fleet covered 125,000 square kilometers in 138 days—a coverage rate that dwarfs anything the Navy’s organic MCM force can achieve. The company’s capacity to deploy multiple AUVs from a single vessel, operating in coordinated search patterns with overlapping sonar coverage, represents precisely the throughput multiplier that the Hormuz clearance operation lacks.

Exail UMIS (formerly ECA Group, La Garde, France). While Exail’s UMIS is a military system, its architecture demonstrates how commercial robotic technology translates to mine warfare. The Belgian-Dutch rMCM program—12 purpose-built MCM motherships, approximately 100 drones, and containerized C2 systems, under a €2 billion contract awarded in 2019—uses Exail’s Inspector 125 unmanned surface vehicle to deploy and recover A18-M AUVs equipped with UMISAS interferometric synthetic aperture sonar, T18-M towed sonars, Seascan mine identification ROVs, and K-Ster expendable mine disposal vehicles. The first vessel, M940 Oostende, was delivered to the Belgian Navy in November 2025, and the first MCM toolbox was delivered to Zeebrugge in March 2026. NATO’s NSPA has separately ordered K-Ster mine neutralization vehicles for European navies. This is the system architecture the U.S. Navy should have emulated: purpose-built MCM motherships operating organic drone swarms, with the unmanned systems designed as a system-of-systems from inception rather than bolted onto a multi-mission hull.

Other Applicable Platforms. Forum Energy Technologies’ XLX EVO III work-class ROVs, widely used in Nigerian and North Sea offshore operations, carry manipulator arms and high-definition cameras suitable for mine identification and could be adapted for mine neutralization with explosive charges. Saab’s AUV62-MR, currently being fitted with Klein Marine Systems synthetic aperture sonar for the Swedish Navy, bridges the defense-commercial divide directly. Teledyne Marine’s Gavia and Slocum AUV families are used by both commercial survey operators and allied navies for mine-like object detection. VideoRay, recently acquired by BlueHalo, manufactures small ROVs used for mine identification by multiple navies.

The Integration Challenge

Commercial AUVs cannot simply be pressed into MCM service overnight. The gaps are real: commercial side-scan sonar resolution may not match purpose-built mine-hunting sonar for classifying small, low-contrast targets like the Maham-7; mine identification requires trained analysts and validated target-recognition algorithms tuned to specific mine types; mine neutralization requires either expendable vehicles (Barracuda, K-Ster) or EOD divers, neither of which the commercial sector provides; and operating in a contested environment with IRGC threats requires force protection that no commercial operator is equipped to provide.

However, the most time-consuming phase of mine clearance is the initial area survey—the systematic, exhaustive scanning of seabed to detect and classify all objects of interest. This is precisely the task at which commercial AUV fleets excel. A concept of operations in which commercial HUGIN or Ocean Infinity AUVs conduct the initial wide-area survey, with Navy ExMCM companies and LCS MCM packages focusing on reacquisition, identification, and neutralization of confirmed contacts, would dramatically increase the throughput of the entire clearance operation. The commercial survey phase does not require the vehicles to enter a defended minefield under fire; it requires them to methodically image the seabed, which is their daily business.

The legal and contractual mechanisms exist. The Defense Production Act, Maritime Administration sealift agreements, and existing commercial contracts with firms like Ocean Infinity (which already works with the U.S. government on oceanographic projects) provide frameworks for rapid commercial augmentation. The British Royal Navy has already piloted commercial-military MCM integration through Project Wilton and its Maritime Autonomous Systems Trials (MAST) program. What is lacking is not capability but institutional imagination—and, characteristically for the mine warfare community, advance planning.

Assessment

The Navy’s transition to an unmanned-centric mine warfare force was conceptually sound. Keeping sailors out of minefields, extending sensor reach through autonomous platforms, and building a modular, deployable kill chain from detection through neutralization all represent genuine advances in mine warfare doctrine. The problem is not the vision but the execution timeline. The service retired proven, purpose-built platforms before their unmanned replacements had been validated in operationally relevant conditions.

The Knifefish has never operated against real mines in a contested environment. The Fleet-class USV’s reliability problems were documented by the Navy’s own training command. The Barracuda completed its first autonomous test nine months ago. The ALMDS excels in clear water against near-surface moored mines but cannot see bottom mines in turbid conditions. The AN/AQS-20C sonar, towed by a USV that requires six hours of preparation per mission, has exhibited data-recording failures that go undetected until post-mission analysis—meaning an entire sortie can be rendered useless without the crew knowing it.

Meanwhile, the ExMCM companies with their Mk 18 UUVs represent a proven, deployable capability—but one designed for expeditionary operations at harbor scale, not for clearing a 100-nautical-mile strait against a dynamic mine threat while under potential fire.

CENTCOM has structured the clearance effort in three phases: area securing, detailed survey with unmanned systems, and creation of a verified safe corridor for commercial shipping. The approach is doctrinally sound. Whether the unmanned systems are operationally ready to execute Phase 2 at the speed, scale, and reliability the mission demands is the question the Navy is about to answer under the most unforgiving conditions possible.

The Foreign Policy Research Institute and the April 2026 Proceedings have both noted that Belgium and the Netherlands jointly developed an advanced MCM capability—the rMCM program—centered on dedicated MCM vessels operating autonomous underwater and surface vehicles with high-definition synthetic aperture sonar. Neither nation has committed those assets to the strait. The broader lesson is clear: unmanned mine warfare is a coalition-wide gap, not merely an American one.

For the Navy, the Strait of Hormuz is now a live-fire examination of a force structure built on unmanned platforms. The systems will either prove their worth or expose a generational miscalculation in mine warfare investment. The 130 ships per day that once transited this chokepoint—carrying one-fifth of the world’s oil—are waiting for the answer.

Sources

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21. “Mine Warfare: Ready and Able Now.” Proceedings, Vol. 144/9/1,387, September 2018. usni.org/magazines/proceedings
22. RTX / Raytheon. “RTX’s Raytheon demonstrates autonomous capabilities of its Barracuda mine neutralizer.” Press release, 8 July 2025. rtx.com
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24. Keller, John. “Raytheon to build unmanned underwater vehicle (UUV) with explosive warhead to destroy or disable ocean mines.” Military & Aerospace Electronics, June 2022. militaryaerospace.com
25. Northrop Grumman. “Airborne Laser Mine Detection System (ALMDS).” Product page. northropgrumman.com
26. Northrop Grumman. “Northrop Grumman Delivers First ALMDS Pod to Republic of Korea.” Press release, 15 October 2025. news.northropgrumman.com
27. Eckstein, Megan. “Navy nears operational capability on LCS counter-mine mission package.” Defense News, 31 January 2023. defensenews.com
28. Eckstein, Megan. “What’s ahead for Navy unmanned underwater vehicle programs?” Defense News, 29 November 2022. defensenews.com
29. Leidos. “Leidos is developing a new generation of underwater drones.” 20 September 2022. leidos.com
30. Keller, John. “Leidos to develop medium-sized unmanned underwater vehicle.” Military & Aerospace Electronics, July 2022. militaryaerospace.com
31. “Leidos Awarded Navy Contract Modification for Medium UUV Development.” ExecutiveBiz, 20 July 2023. executivebiz.com
32. “Navy awards several contracts focused on LCS mine countermeasures.” Inside Defense, 5 February 2025. insidedefense.com
33. “US Navy Orders Additional LCS MCM USVs.” SeaWaves Magazine, 3 February 2025. seawaves.com
34. Al Jazeera. “What do we know about sea mines in and around the Strait of Hormuz?” 13 April 2026. aljazeera.com
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36. “Iran Can’t Find Its Own Hormuz Mines, U.S. Says.” House of Saud, 11 April 2026. houseofsaud.com
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38. “How Iran’s naval mines could choke global trade through the Strait of Hormuz.” Arab News, 7 April 2026. arabnews.com
39. NAVSEA. “LCS Mission Modules Program.” SNA 2022 brief. navsea.navy.mil (PDF)
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42. Grieco, Kelly, and Brad Westermann. “Five Things to Know About Iranian Minelaying.” Stimson Center, April 2026. stimson.org
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44. CNN. “Iran begins laying mines in Strait of Hormuz, sources say.” 10 March 2026. cnn.com
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51. “Decision and Disaster at the Dardanelles.” Naval History, Vol. 39, No. 2, April 2025. usni.org/magazines/naval-history
52. “Mines lesson from the Dardanelles 1915.” Australian Naval Institute, March 2026. navalinstitute.com.au
53. “Sweeping the Dardanelles.” Naval Historical Society of Australia. navyhistory.au
54. Phillips-Levine, Trevor, et al. “How the Navy Can Avoid a 21st-Century Gallipoli.” Proceedings, Vol. 148/1/1,427, January 2022. usni.org/magazines/proceedings
55. Marolda, Edward J. “The Siege of Wonsan.” Naval History, Vol. 37, No. 4, August 2023. usni.org/magazines/naval-history
56. Cox, Samuel J. “H-055-1: Wonsan: October–November 1950.” NHHC Director’s Corner, October 2020. history.navy.mil
57. “Clearing the Way to Wonsan.” Naval History and Heritage Command, 2018 Essay Contest. history.navy.mil
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59. Kongsberg Discovery. “HUGIN Autonomous Underwater Vehicles.” Product portfolio. kongsberg.com
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61. Kongsberg Discovery. “HUGIN Superior AUV.” Product page. kongsberg.com
62. Ocean Infinity. “Technology.” Company overview. oceaninfinity.com
63. “Ocean Infinity Takes Delivery of Final Survey Vessel in Its New High-Tech Fleet.” Offshore Wind, 15 December 2025. offshorewind.biz
64. Ferguson, Elaine. “Autonomy: Inside the Building of Ocean Infinity’s Armada Fleet.” Marine Technology News, February 2021. marinetechnologynews.com
65. Exail. “UMIS—Unmanned MCM Integrated System.” Product page. exail.com
66. Exail. “Belgium Naval & Robotics delivers first MCM toolbox for the Belgian-Dutch rMCM programme.” Press release, March 2026. exail.com
67. Naval Group. “Naval Group launches the first mine countermeasure vessel of the Belgian-Dutch rMCM programme.” Press release. naval-group.com
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The views expressed are the author’s own and do not represent those of any government agency or defense contractor. Factual claims are sourced from the referenced publications and official releases as of 14 April 2026.

Zumwalt-class destroyers may receive SPY-6 radars from frigates - Naval News

Zumwalt-class destroyers may receive SPY-6 radars from frigates - Naval News

Retrofitting Failure: The Zumwalt-Class and the $32 Billion Learning Curve

How the Navy finally might turn a failed gun platform into a usable warship—if it keeps changing everything

BOTTOM LINE UP FRONT

The U.S. Navy is evaluating a proposal to retrofit AN/SPY-6 radar systems—originally manufactured for the cancelled Constellation-class frigate program—onto all three operational Zumwalt-class destroyers as part of the Zumwalt Enterprise Upgrade Solution (ZEUS). Raytheon has received Navy funding to develop combat management system modifications enabling SPY-6 integration, while both contractors and Navy officials have expressed confidence in the technical feasibility. The SPY-6(V)3 variant, dimensionally comparable to the incumbent AN/SPY-3, could be installed without major structural modifications; however, no final decision has yet been made. The backfit represents one element of a broader strategic pivot to transform the Zumwalts from their failed original concept as gun-armed littoral platforms into long-range hypersonic strike assets aligned with the wider Aegis fleet.

The Zumwalt Class in Transition

The Zumwalt-class destroyers represent one of the U.S. Navy's most dramatic strategic reversals. Originally envisioned as a 32-ship class optimized for naval surface fire support (NSFS) in shallow-water operations, the platform's distinctive tumblehome hull and composite deckhouse were engineered to achieve radar cross-section comparable to that of a fishing boat—approximately fifty times more difficult to detect than a conventional destroyer.¹ However, rising costs for the Long-Range Land-Attack Projectile (LRLAP) ammunition essential to the ship's core mission rendered the 155-millimeter Advanced Gun System economically unsustainable, and procurement was cancelled well before the first ship's commissioning.

With only three ships authorized and built—USS Zumwalt (DDG-1000), USS Lyndon B. Johnson (DDG-1002), and USS Michael Monsoor (DDG-1001)—the Navy has radically reoriented the class toward extended-range strike warfare. Beginning in 2023, both AGS turrets were removed from each destroyer and replaced with vertical launch system (VLS) cells accommodating the Conventional Prompt Strike (CPS) hypersonic missile system.² USS Zumwalt completed this conversion in late 2025, and now carries twelve CPS missiles in four Advanced Payload Modules forward of the superstructure.³ USS Lyndon B. Johnson is undergoing similar modifications at Ingalls Shipbuilding in Pascagoula, while USS Michael Monsoor is scheduled for conversion during its next maintenance availability.

The CPS missile, jointly developed by the Army and Navy, achieves Mach 5+ velocity and delivers a Common Hypersonic Glide Body (C-HGB) across ranges exceeding 1,725 nautical miles—a dramatic capability expansion compared to the AGS's notional 63-nautical-mile range.^4,5 This transformation has effectively shifted the Zumwalt-class from a littoral gun platform to a strategic-depth strike destroyer, fundamentally altering the operational calculus for the ships' remaining service life.

The Combat System Modernization: ZEUS

Recognizing that hypersonic strike capability alone would not suffice for twenty-first-century fleet operations, the Navy initiated the Zumwalt Enterprise Upgrade Solution (ZEUS)—a comprehensive combat system modernization program first formally outlined in a Request for Information (RFI) issued in November 2022.⁶ ZEUS encompasses far more than radar replacement alone. The program includes integration of the Surface Electronic Warfare Improvement Program (SEWIP), the undersea warfare combat system SQQ-89, and the Cooperative Engagement Capability (CEC) datalink—measures designed to align the Zumwalt-class more closely with the Aegis-equipped fleet standard and enhance network-centric warfare integration.^7,8

The radar upgrade component reflects a critical shortcoming in the original Zumwalt design. The AN/SPY-3 multifunction radar, while performing well in its X-band search and track role, was never intended to shoulder the full burden of air defense alone. Zumwalt-class destroyers were originally equipped with a dual-band radar architecture pairing the SPY-3 with the AN/SPY-4 S-band volume search radar. However, in June 2010, Pentagon acquisition officials elected to delete the SPY-4 as a cost-reduction measure, requiring the SPY-3 to be reprogrammed to perform both horizon search and volume search functions simultaneously—a compromise that limits its capability to manage large-scale air attacks while providing fire control for multiple simultaneous engagements.^9,10 The SPY-3 also lacks integration with modern ballistic missile defense systems, a growing liability as the Navy faces advanced cruise-missile and hypersonic threats.

The AN/SPY-6: A Generation Forward

The AN/SPY-6 represents the latest generation of Raytheon naval radar technology. First delivered to the Navy in July 2020, the SPY-6 is built on a modular, scalable architecture employing Radar Modular Assemblies (RMAs)—self-contained radar modules, each approximately two feet per side, that function as individual transmit/receive elements.¹¹ This modular approach enables the Navy to field multiple variants optimized for specific platforms and mission sets, ranging from the full four-sided SPY-6(V)1 system aboard Flight III Arleigh Burke-class destroyers (with 37 RMAs per face) to more compact configurations for smaller combatants.

The SPY-6(V)3 configuration under consideration for Zumwalt-class integration employs a three-sided phased array, each with nine RMAs, providing volume search and track capabilities across extended detection ranges and advanced electronic scanning performance characteristic of modern AESA radar systems.^11,12 The SPY-6(V)3 is already planned for installation on Constellation-class frigates (for ships remaining under construction) and serves as the primary air and missile defense radar aboard Gerald R. Ford-class aircraft carriers beginning with USS John F. Kennedy (CVN-79).¹¹ This commonality across platform classes has significant implications for fleet logistics, training, and maintenance.

The SPY-6 system offers approximately 15 decibels improved sensitivity compared to the SPY-1 radar architecture that equips the Aegis fleet—equivalent to detecting targets half the size at twice the distance—and provides simultaneous defense against ballistic missiles, cruise missiles, air and surface threats, plus organic electronic warfare capability.¹¹ Integration with CEC enables true network-centric air defense, where each ship's SPY-6 radar data is fused with information from surrounding platforms to create a composite battlespace picture far superior to what any single ship could achieve in isolation.

The Constellation-Class Cancellation: An Unexpected Opportunity

The Constellation-class frigate program, awarded to Fincantieri Marinette Marine in April 2020, was conceived as a more affordable complement to the DDG-51 Arleigh Burke-class destroyer. The design was based on a scaled adaptation of Marinette's FREMM (Frigate European Multi-Mission) platform, itself a derivative of the Italian FREMM design with extensive Americanization to meet Navy survivability and electromagnetic requirements.^13,14 However, the program encountered cascading delays. As of April 2024, the lead ship, USS Constellation (FFG-62), was only 10 percent complete, with the Navy's FY2026 budget projecting delivery slipping from the original 2026 target to April 2029—a delay of 36 months at an estimated cost of $1.5 billion.^14,15 The Government Accountability Office identified fundamental design stability issues, with the ship becoming significantly heavier than anticipated and achieving far less than the promised cost advantage over the larger, more capable DDG-51.

On 25 November 2025, Secretary of the Navy John C. Phelan cancelled all but the first two ships in the Constellation-class program as part of a comprehensive Navy fleet strategy review.¹⁶ At the time of cancellation, the lead frigate was reported 12 percent complete. The Navy elected to complete the two ships under construction (FFG-62 and FFG-63) to preserve Marinette Marine's industrial capacity and maintain continuity of shipyard employment, but halted procurement of the remaining four ships on contract. The Navy subsequently announced a new frigate competition for a smaller, faster-to-build design based on the U.S. Coast Guard's National Security Cutter (NSC) hulform, designated FF(X)—an architecture explicitly not optimized for the SPY-6 radar due to size constraints.^16,17

This cancellation decision created a windfall of surplus long-lead-time items manufactured for the Constellation-class program. According to John Tobin, Associate Director for International SPY Radar Programs at Raytheon, SPY-6(V)3 radar arrays originally procured for the cancelled frigates remain in inventory. Raytheon and Navy officials have indicated that these systems could be repurposed and installed on the Zumwalt-class at considerably lower total cost than procuring entirely new radar suites.⁶ The decision to salvage these components represents pragmatic asset stewardship in an environment of fiscal constraint.

Technical and Programmatic Feasibility

Raytheon officials have expressed confidence in the technical feasibility of the SPY-6 backfit. Jennifer Gauthier, Vice President of Naval Systems & Sustainment at Raytheon, stated in an interview conducted in Tokyo in May 2026 that "we are currently in discussions with the U.S. Navy and nothing has been decided," while elaborating on Raytheon's ongoing development efforts. Importantly, she confirmed that Raytheon had received Navy funding for development work on the Zumwalt combat management system specifically intended to enable SPY-6 integration, and that the company had established "the first certified, classified software factory for Zumwalt" enabling rapid, secure software uploads to the ships without the extended procurement and testing cycles traditionally required for fleet updates.⁶

From a physical integration perspective, the SPY-6(V)3 is dimensionally comparable to the incumbent SPY-3. Tobin noted that the SPY-3 is "roughly comparable in size" to the SPY-6(V)3 configuration of nine RMAs, suggesting that physical installation would not require substantial deckhouse modifications or structural rework.⁶ This point is significant; the Zumwalt-class composite deckhouse is one of the ship's most complex and costly structural elements, and any extensive modification would substantially increase backfit cost and risk schedule slippage.

The Navy has signaled its commitment to the modernization path through concrete funding actions. On 20 April 2026, the Navy awarded Raytheon a $213.4 million contract modification for continuation of Zumwalt-class combat system integration, modernization, installation, testing, and sustainment through 2027.⁸ This funding supports development activities intended to prepare the ships for future upgrades and demonstrates sustained naval commitment to keeping the Zumwalts at an acceptable combat readiness level throughout their operational lifespans.

Strategic and Doctrinal Implications

The SPY-6 backfit should not be viewed in isolation, but rather as a single element in a comprehensive effort to transform the Zumwalt-class from an aberrant platform pursuing a failed operational concept into an integrated member of the twenty-first-century fleet. The combination of hypersonic strike capability (via CPS), improved air and missile defense (via SPY-6), undersea warfare integration (via SQQ-89), modern electronic warfare systems (via SEWIP), and network-centric capability (via CEC) would position the Zumwalt-class as a formidable multi-mission platform capable of fulfilling strike, air defense, and information-warfare roles across the operational spectrum.

The three Zumwalt-class destroyers are expected to remain in service for decades. Without modernization, they would become increasingly obsolete, representing a diminishing return on the $32 billion invested in the class's research, development, and construction. The ZEUS program, including the SPY-6 backfit, represents the most cost-effective path to preserving their relevance and utility within the constrained fiscal environment the Navy now inhabits.

Critically, the SPY-6 backfit would enhance the Navy's ability to operate in contested environments. The improved detection range, ballistic missile defense capability, and network-centric integration afforded by the SPY-6 would significantly increase the Zumwalts' survivability in scenarios involving near-peer competitors equipped with advanced antiship cruise missiles and ballistic-missile threats. In the context of potential Pacific operations against peer adversaries, every incremental improvement in sensor capability and defensive integration carries strategic weight.

Remaining Uncertainties and Next Steps

No final decision has yet been made regarding the SPY-6 backfit. Navy officials and Raytheon representatives alike characterize the current phase as one of active dialogue and development work, with no commitment to proceed. Several factors will likely influence the Navy's ultimate decision: the outcome of ongoing ZEUS integration testing and combat management system development; the final cost estimate for the backfit across three ships; schedule implications relative to other competing modernization priorities; and the availability of repurposed SPY-6(V)3 arrays from the cancelled Constellation-class program as the Navy completes those two remaining frigates and assesses its actual surplus inventory.

The Congressional Research Service and Government Accountability Office will likely scrutinize any decision to proceed, particularly given Congress's longstanding concerns over Zumwalt-class cost overruns and program management. The Navy will need to make a compelling case that the SPY-6 backfit represents a prudent investment in fleet readiness rather than merely throwing additional resources at a historically troubled program.

The most likely scenario involves a phased approach, with USS Zumwalt receiving the initial SPY-6 installation during a future deployment to sea availability, followed by USS Lyndon B. Johnson and USS Michael Monsoor in subsequent modernization periods. This approach would allow the Navy to validate integration, test operational employment, and refine procedural and training requirements while preserving the ability to adjust subsequent installations based on lessons learned.

Conclusion

The potential backfit of AN/SPY-6 radar systems to the Zumwalt-class destroyers represents a pragmatic response to both technical shortcomings in the original design and fiscal realities that preclude procuring entirely new sensor suites. By leveraging surplus systems from a cancelled competitor program, the Navy can modernize three aging platforms at a fraction of the cost of new-build radar integration. The technical feasibility appears sound, contractor development efforts are well underway with Navy financial support, and senior Navy officials have expressed optimism regarding the modernization path.

What began as a technology-demonstrator for naval gun fire support has been progressively transformed—first into a littoral surface fire support platform, then into a hypersonic strike destroyer, and now potentially into a network-integrated multi-mission combatant capable of holding its own within the modern fleet. The SPY-6 backfit, if approved and executed successfully, would represent the final critical piece of that transformation, converting the troubled Zumwalt-class from a symbol of failed innovation into a capable platform suited to contemporary naval warfare. Whether the Navy ultimately commits to the backfit will reveal much about the service's willingness to invest in the long-term modernization of existing platforms rather than perpetually pursuing new starts.

A Final Irony

The arc of the Zumwalt-class offers a cautionary lesson in defense acquisition. The original concept—a gun-armed littoral strike platform with stealth features—was sound in theory but ultimately unsustainable: the Long-Range Land-Attack Projectile proved economically ruinous, the gun-centric mission concept lost political support, and only three ships were ever built instead of the planned 32. What the Navy is now contemplating—a long-range hypersonic strike destroyer with SPY-6 air defense and network integration—bears almost no resemblance to what was originally approved. Yet after fifteen years and more than $32 billion in sunk costs, after multiple complete mission redesigns, and after stripping out systems and bolting on others, the Zumwalts may finally achieve utility as capable twenty-first-century surface combatants. The tragic irony is that three Flight III Arleigh Burke-class destroyers, equipped with SPY-6 and conventional strike missiles from their inception, would have cost considerably less and delivered equivalent or superior capability a decade earlier. The Navy has, in effect, paid dearly for a protracted learning experience conducted aboard billion-dollar warships. If the SPY-6 backfit proceeds and succeeds, the Zumwalts will vindicate themselves not through faithfulness to their original design concept, but through the Navy's willingness to change course fundamentally and repeatedly until three troubled platforms finally become genuinely useful assets. That is a hard-won but valuable lesson for an institution that struggles with admitting error and altering course.

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7. "Repurposing the US Navy's Zumwalt-class destroyers with hypersonic strike capability." Navy Lookout, August 21, 2025. https://www.navylookout.com/repurposing-the-us-navys-zumwalt-class-destroyers-with-hypersonic-strike-capability/

8. "U.S. Navy Considers Replacing Zumwalt-Class SPY-3 Radars with SPY-6 from Cancelled Frigate Program." The Defense News, May 1, 2026. https://www.thedefensenews.com/news-details/US-Navy-Considers-Replacing-Zumwalt-Class-SPY-3-Radars-with-SPY-6-from-Cancelled-Frigate-Program/

9. AN/SPY-3. Wikipedia. Retrieved May 2026. https://en.wikipedia.org/wiki/AN/SPY-3

10. "Dual Band Radar Swapped Out In New Carriers." Defense News, March 17, 2015. https://www.defensenews.com/naval/2015/03/17/dual-band-radar-swapped-out-in-new-carriers/

11. AN/SPY-6. Wikipedia. Retrieved May 2026. https://en.wikipedia.org/wiki/AN/SPY-6

12. "The Navy a Hypersonic Plan to Save the Stealth Zumwalt-Class Destroyers." National Security Journal, September 5, 2025. https://nationalsecurityjournal.org/the-navy-a-hypersonic-plan-to-save-the-stealth-zumwalt-class-destroyers/

13. Constellation-class frigate. Wikipedia. Retrieved May 2026. https://en.wikipedia.org/wiki/Constellation-class_frigate

14. "Navy Cancels Constellation-class Frigate Program." USNI News, November 25, 2025. LaGrone, Sam. https://news.usni.org/2025/11/25/navy-cancels-constellation-class-frigate-program-considering-new-small-surface-combatants

15. Navy Constellation (FFG-62) and FF(X) Class Frigate Programs: Background and Issues for Congress. Congressional Research Service (R44972), March 16, 2026. https://www.congress.gov/crs-product/R44972

16. "The US Navy Just Scuttled the Constellation-Class Frigate Program." The National Interest, November 26, 2025. https://nationalinterest.org/blog/buzz/us-navy-just-scuttled-constellation-class-frigate-program-ps-112625

17. "U.S. Navy retains first six Constellation-class frigates in FY2026 budget to strengthen fleet coverage." Army Recognition, July 7, 2025. https://www.armyrecognition.com/news/navy-news/2025/us-navy-retains-first-six-constellation-class-frigates-in-fy2026-budget-to-strengthen-fleet-coverage

 

The Claude Token Efficiency Playbook - Get More for your Money

 

26 Token Optimization Techniques: Quick Reference

High-Impact Strategies (40%+ savings)

1. Replace PDFs with Markdown – Convert PDFs to Google Docs, export as .md. Saves 85–90% vs raw PDF.
2. Use Projects for Shared Files – Upload once, reference across multiple chats. Saves 80%.
3. Batch Tasks Into One Message – Ask 3 things at once instead of 3 messages. Saves 56%.
4. Trim Personal Context to <2K Words – Bloated context files waste 10% of every conversation. Saves 70%.
5. Compress Intermediate Outputs – After analysis, summarize into bullet points for reference in follow-ups. Saves 67%.
6. Batch Similar Queries with Caching – Ask all related questions about one system in one chat. Saves 87.5%.

Medium-Impact Strategies (25–40% savings)

7. Right-Size Models – Use Haiku for simple tasks, Sonnet for standard work, Opus only for deep reasoning. Saves 50% when applied systematically.
8. Write Short Prompts (<30 words) – Brief, clear prompts reduce re-read overhead. Saves 33%.
9. Specify Output Format Upfront – "JSON table with columns X, Y, Z" prevents reformatting requests. Saves 60%.
10. Show Your Thinking First – Ask Claude to self-critique in initial response, reducing revision cycles. Saves 40–50%.
11. Specify Constraints Upfront – "Under 500 words," "3 bullet points," "1-paragraph summary" prevents scope creep. Saves 73%.
12. Use Checkpoints Every 5–7 Messages – Ask "Are we on track?" in complex conversations to catch wrong paths early. Saves 83%.
13. Edit Instead of Correcting – Click Edit on your message, fix it, regenerate. Don't stack "Actually, I meant…" messages.
14. Use New Chats for Different Topics – One topic per chat. Separate chats avoid re-reading irrelevant context. Saves 40% in multi-topic conversations.
15. Disable Tools by Default – Tools add 200–400 token overhead per exchange even when unused.
16. Restart Conversations Every 15–20 Messages – Long conversations accumulate re-read overhead. Saves 55%.
17. Search Before Asking – Use conversation search to find past solutions. Saves 67–75% if found.
18. Crop Screenshots Tightly – Crop to only the relevant portion. Full screenshot = 1,300 tokens; tight crop = 50 tokens.
19. Chain Tasks in One Message – "Analyze this data, then write a summary from your analysis" instead of separate messages. Saves 30%.
20. Use "Assume You Know" References – After establishing context, reference it: "Assume you know the CONVERGE-01 trial from earlier." Saves 75%.
21. Use Negative Constraints – "Explain without covering basics I already know" is clearer than restating what you know.
22. Outline Mode Before Full Detail – Ask for pseudocode/outline first, expand only necessary sections. Saves 20–40%.
23. Pre-Process Data Externally – Clean data before uploading (Excel, Python). Saves 60–75%.
24. Build Conditional Templates – Create reusable templates with [IF: condition] sections for different use cases. Saves 40–50%.
25. Project Status Summaries – At session end, ask Claude to write a status summary. Paste it next session instead of re-explaining. Saves 72%.

---

Implementation Roadmap

Week 1: Quick Wins (Save ~30%)

• Technique 7: Right-size models
• Technique 8: Write shorter prompts
• Technique 18: Crop screenshots

Week 2: Process Changes (Additional 20%)

• Technique 3: Batch tasks
• Technique 14: Separate chats for topics
• Technique 13: Edit instead of correcting

Week 3: Structural Setup (Additional 25%)

• Technique 1: PDF → Markdown
• Technique 2: Projects for shared files
• Technique 4: Trim personal context

Week 4: Advanced Optimization (Additional 15%)

• Technique 15: Tool management
• Technique 24: Prompt templates with conditions
• Technique 9: Restart long conversations

Expected total improvement: 70–80% token efficiency gain

---

The Core Principle

Token efficiency is a systems problem, not a single-query problem. Efficient workflows:

• Build around one Project per major work area (IPCSG research, technical analysis, civic policy)
• Use persistent templates and shared files across chats
• Create continuity with status summaries and checkpoints
• Batch related work together to leverage prompt caching

Individual tips help. But combining them into a system-level workflow is what really multiplies savings across months of work.

---

Quick Wins Summary

Technique	Savings	Effort
Replace PDFs with .md	85–90%	Low
Use Projects	80%	Low
Batch tasks	56%	Low
Right-size models	50%	Medium
Trim context	70%	Medium
Short prompts	33%	Low
Compress outputs	67%	Low
Checkpoints	83%	Low
Batch with caching	87.5%	Medium
Pre-process data	60–75%	Low

Start with the "Low Effort" column. You'll hit 50%+ savings in Week 1.

Techniques to Stop Hitting Claude's Limits: Details

Claude's token limits aren't arbitrary walls—they're guardrails that force discipline. Every token you waste on redundant uploads, verbose prompts, or context bloat is a token stolen from actual work. This article translates raw optimization techniques into a workflow that scales.

The Problem: How Users Burn Tokens

Most Claude users operate at 30–50% efficiency. A 200K token limit sounds generous until you realize:

• A 10-page PDF = 15,000–30,000 tokens gone before you type anything

• A 400-word prompt gets re-read 20+ times across a conversation

• Three sequential messages force Claude to re-tokenize the entire history three times

• A single bloated personal context file (20K words) loads into every session

• Tools left enabled burn tokens on every exchange, even when unused

For teams, this compounds catastrophically. One poorly optimized workflow × 50 users × 20 chats/month = token hemorrhage that looks like a feature problem when it's actually a process problem.

---

Technique 1: Replace PDFs with Markdown via Google Docs

The Problem PDFs are opaque to token counting. A single page burns 1,500–3,000 tokens depending on layout complexity, images, and formatting. A 20-page technical document = 30,000–60,000 tokens before analysis begins.

The Solution

1. Paste PDF text into a Google Doc

2. Clean up formatting (remove headers, footers, duplicate spacing)

3. Download as .md

4. Upload the markdown file

Token Cost Comparison

• PDF (20 pages): 30,000–60,000 tokens

• Markdown equivalent: 3,000–5,000 tokens

• Savings: 85–90%

Why It Works Markdown is plain text. Claude tokenizes it at ~0.25 tokens per word. PDFs include invisible rendering information, font metadata, and positioning data that all get tokenized. Google Docs' export strips that noise.

When to Use This

• Technical reports, whitepapers, research papers

• Legal documents, contracts, policy briefs

• Any document longer than 3 pages

• Documents with complex formatting or images

When Not To

• Documents requiring exact visual layout (posters, forms with specific spacing)

• Scanned PDFs (use OCR first, then convert)

• Single-page quick references (just copy-paste text directly)

---

Technique 2: Right-Size the Model for the Task

The Problem Opus costs 5x more per token than Haiku and 3x more than Sonnet. Using Opus for summarization or simple coding is like hiring a surgeon to check your blood pressure.

Model Economics | Task | Right Choice | Why | |------|-------------|-----| | Summarize a document | Haiku | 90% accuracy, 1/5 cost | | Write a simple script | Sonnet | Handles most coding, 1/3 Opus cost | | Debug complex reasoning | Opus | Deep chains need depth | | Brainstorm ideas | Haiku | Ideation doesn't need reasoning depth | | Multi-step analysis | Opus | Benefit from extended reasoning | | Customer service reply | Haiku | Template matching, not reasoning |

Decision Tree

• Does this task require multi-step reasoning across 5+ inference steps? → Opus

• Does it need deep technical knowledge but straightforward logic? → Sonnet

• Is it straightforward task execution? → Haiku

Token Budget Impact A team running 50 daily chats:

• All Opus: 50 × 3,000 tokens/chat = 150,000 tokens (expensive baseline)

• Right-sized mix (60% Haiku, 30% Sonnet, 10% Opus): 50 × 1,500 tokens avg = 75,000 tokens

• Daily savings: 75,000 tokens (50% of budget)

---

Technique 3: Batch Tasks Into Single Messages

The Problem Every new message forces Claude to re-read the entire conversation history before responding. Three sequential messages = three full re-reads of context.

Example: The Inefficient Way

Message 1: "Can you summarize this report?"

[Claude responds, tokens consumed]

Message 2: "Now extract the key metrics"

[Claude re-reads entire conversation + new message]

[Claude responds, tokens consumed]

Message 3: "Format those metrics as a table"

[Claude re-reads entire conversation again]

[Claude responds, tokens consumed]

Token Cost: Each message re-reads full history. With a 20-message conversation, message 21 retokenizes all 20 previous exchanges.

The Efficient Way

Message 1: "Do three things:

1. Summarize this report in 3 sentences

2. Extract the top 5 metrics

3. Format those metrics as a table with columns: Metric, Value, Trend"

[Claude responds with all three outputs]

Token Savings

• Inefficient (3 messages): 8,000 tokens (context re-read overhead included)

• Efficient (1 message): 3,500 tokens

• Savings: 56%

How to Batch Effectively

1. List all tasks upfront with numbers

2. Specify output format for each (table, list, paragraph, JSON)

3. Set constraints (word counts, detail level) per task

4. Use one message to capture all context needed

When Not to Batch

• Tasks require Claude's output from task #1 to inform task #2

• Second task is fundamentally different in scope

• You need to iterate on one task before moving to the next

---

Technique 4: Edit Instead of Stacking Corrections

The Problem Users write a message, realize mid-reply they misspoke, and send a follow-up: "Actually, I meant…" This creates bad history that Claude must re-read forever.

Example: Poor Practice

Message 1: "Analyze this dataset with regression analysis"

[Claude responds]

Message 2: "Wait, I said regression but I meant clustering"

[Claude re-reads both messages, applies fix]

Message 3: "Also, use k-means specifically"

[Conversation now has 3 messages for what should be 1]

The Right Way

1. Click the Edit button on your original message

2. Fix the prompt

3. Click Regenerate

Result: Original bad message disappears. Conversation history stays clean. No token waste on corrections.

Token Impact

• Stack of 3 messages with corrections: 5,000 tokens (includes overhead of re-reading bad context)

• Single edited message: 2,000 tokens

• Savings: 60%

---

Technique 5: Use New Chats for New Topics

The Problem One chat drifts across 4 different topics (analyzing a dataset, then drafting an email, then brainstorming ideas, then debugging code). Claude must re-read everything above before every response.

Example: Conversation Bloat

Messages 1-5: Analyze Q3 sales data

Messages 6-10: Draft investor email (unrelated)

Messages 11-15: Brainstorm product features (unrelated)

Messages 16-20: Debug Python script (unrelated)

Message 21: New question about the Python script

[Claude must re-read all 20 previous messages, 80% of which are irrelevant]

Token Cost: Message 21 tokenizes 20 previous exchanges even though only 5 are relevant.

The Right Way

• New topic = new chat

• One chat = one focused problem

Token Impact

• Bloated single chat (4 topics, 20 messages): Each new message re-reads ~8,000 tokens of irrelevant context

• Four separate chats (5 messages each): Each new message re-reads ~2,000 tokens of relevant context

• Savings across 20 total messages: 40,000 tokens (60% of original)

Bonus: Organization becomes much easier. Your chat history is searchable and scannable.

---

Technique 6: Write Short, Clear Prompts (Under 30 Words)

The Problem A 400-word prompt gets re-read dozens of times across a conversation. Each follow-up question forces re-tokenization of the entire prompt.

Example: Inefficient Prompt

"I'm working on a customer support dashboard for our SaaS platform. We need to

display metrics like average response time, customer satisfaction scores, and

ticket volume trends. The interface should be mobile-responsive and include

filters for date range, department, and customer segment. We're using React

and want it to match our existing design system which uses Tailwind CSS. Can

you help me build this?"

[357 words]

Every follow-up question re-reads all 357 words.

Efficient Version

"Build a customer support dashboard in React: metrics (response time, 

satisfaction, volume), filters (date, dept, segment), mobile-responsive, Tailwind CSS."

[27 words]

Claude asks clarifying questions if needed. You provide details only for what's unclear.

Token Cost Comparison

• Long prompt + 10 follow-ups: Prompt gets re-read 10+ times = 3,000+ tokens of re-reads alone

• Short prompt + 10 clarifications: Clarifications cost ~200 tokens each, total ~2,000

• Savings: 33%

Structure for Short Prompts

1. Action verb (Build, Analyze, Compare, Draft)

2. Deliverable (React component, SQL query, essay outline)

3. Key constraints (3 bullets max)

4. Format (JSON, markdown table, code block)

When to Break This Rule

• First message to a new chat (more context helps)

• Highly specialized domains where brevity creates ambiguity

• Chats where you've established context already

---

Technique 7: Use Projects to Share Files Across Chats

The Problem You upload the same document to 5 different chats. That document gets tokenized in full for each chat. A 10K-token document = 50K tokens burned unnecessarily.

Example: Inefficient Workflow

Chat 1: Upload quarterly_report.pdf → analyze revenue

[10,000 tokens to tokenize document]

Chat 2: Upload quarterly_report.pdf → analyze expenses

[10,000 tokens to tokenize same document again]

Chat 3: Upload quarterly_report.pdf → extract metrics

[10,000 tokens to tokenize same document again]

Total: 30,000 tokens for same document

Projects Solution

1. Create a Project called "Q3 Analysis"

2. Upload quarterly_report.pdf once to the project

3. Every chat in that project references the document automatically

4. Document tokenized once, referenced in every chat

Token Cost

• Without Projects (5 chats with same document): 50,000 tokens

• With Projects: 10,000 tokens (document tokenized once)

• Savings: 80%

Bonus Features

• Team collaboration (everyone in project sees same files)

• Shared context (no redundant uploads)

• Better organization (related chats grouped)

• Prompt caching (reused prompts inside projects don't re-tokenize)

Project Structure Example

Project: "San Diego Transit Analysis"

├─ Files: SANDAG_2024_data.md, MTS_budget.csv, transit_report.pdf

└─ Chats:

    ├─ "Q1 ridership analysis"

    ├─ "Budget efficiency comparison"

    ├─ "Future capacity planning"

    └─ "Funding mechanisms"

All chats reference the same files. No redundant uploads.

---

Technique 8: Disable Tools and Connectors When Not In Use

The Problem Tools consume tokens on every exchange, even when inactive. Web search, calculator, file operations—if enabled, Claude considers them on every response.

Token Cost of Enabled Tools Enabling 3 tools (web search, code execution, file creation) adds ~200–400 tokens overhead per exchange, even when unused.

• 20-message chat with tools enabled: 20 × 300 = 6,000 tokens overhead

• Same chat with tools disabled: 0 tokens overhead

• Savings: 6,000 tokens per 20-message chat

Best Practice

1. Disable all tools by default

2. Enable only the specific tool(s) needed for current task

3. Disable when task completes

Tools to Keep Disabled Most of the Time

• Web search (enable only when asking current events)

• Code execution (enable only during debugging/testing)

• File creation (enable for artifact generation, disable for Q&A)

• Connectors (enable only when accessing Gmail/Calendar/Drive)

Tools Worth Keeping On

• Within Projects that specifically need them

• During focused work sessions where they're used consistently

---

Technique 9: Restart Conversations Every 15–20 Messages

The Problem At message 25, Claude re-reads all 24 previous messages before responding. By message 50, the context window overhead becomes significant.

Context Re-Read Cost

• Message 10: Re-read ~4,000 tokens of context

• Message 20: Re-read ~8,000 tokens of context

• Message 30: Re-read ~12,000 tokens of context

• Message 50: Re-read ~20,000 tokens of context

Solution: Refresh Every 15–20 Messages

1. At ~message 15, summarize key points in a new message: "Summary: We've analyzed X, decided on Y, next step is Z"

2. Start a new chat with that summary as context

3. New chat begins fresh without re-reading all history

Token Impact

• Single 50-message conversation: ~100,000 tokens (with re-read overhead)

• Two 25-message conversations: ~60,000 tokens (less re-read overhead)

• Three 17-message conversations: ~45,000 tokens (minimal re-read overhead)

• Savings: 55%

When to Restart

• Task fundamentally shifts direction

• Conversation length approaches 30+ messages

• New day/session (fresh start feels cleaner)

When Not To

• You need full context from all prior messages for current task

• You're iterating on something that needs complete history

---

Technique 10: Crop Screenshots to Only Relevant Portions

The Problem Users upload full 1000×1000 pixel screenshots when a 200×300 pixel crop would work. Full screenshots tokenize at ~1,300 tokens; crops can drop below 100.

Token Cost of Screenshots

• Full screenshot (1000×1000): ~1,300 tokens

• Medium crop (400×400): ~200 tokens

• Tight crop (200×200): ~50 tokens

• Potential savings: 96%

Example: The Inefficient Way User pastes full desktop screenshot showing:

• Entire taskbar

• Application menu

• Status bar

• And the actual error dialog in bottom right

Claude tokenizes all of it.

The Efficient Way Crop to just the error dialog:

[Cropped to 250×150 pixels]

Claude gets the information without the noise.

Cropping Checklist

• 

  Remove any UI chrome (taskbars, menus) unless relevant

• 

  Remove whitespace margins

• 

  Crop to the minimal bounding box that includes the issue

• 

  Keep just enough context for understanding (one surrounding line/button)

Tools

• Windows: Snip & Sketch (Win+Shift+S)

• Mac: Cmd+Shift+4 (drag to select area)

• Linux: Flameshot or built-in tool

• Online: Snipping tools in browser

---

Technique 11: Build and Reuse Prompt Templates

The Problem Users rewrite similar prompts from scratch repeatedly. Each rewrite is slightly different, prevents caching, and burns mental energy.

Example: Inefficient Rewriting

Chat 1: "Write a technical analysis of the MQ-9B SeaGuardian focusing on 

operational range, sensor capabilities, and integration with naval systems."

Chat 2 (weeks later): "Can you analyze the GA-ASI Gambit system? I want to 

understand its operational capabilities, sensor suite, and how it fits into 

the broader defense architecture."

Chat 3 (another week): "Technical overview of the V-22 Osprey: what it does, 

what sensors it has, and how it works with other military systems."

Same structure, different words each time. Prevents caching.

Prompt Template Approach Create a template in a document:

# Technical System Analysis Template

Analyze [SYSTEM_NAME] and cover:

1. Operational range and endurance

2. Sensor suite and detection capabilities

3. Integration with broader force architecture

4. Notable operational history or incidents

5. Key limitations or known issues

Format as: summary section + detailed technical breakdown + 

comparison table with similar systems.

Reuse for every similar analysis:

Chat 1: Analyze MQ-9B SeaGuardian [use template]

Chat 2: Analyze GA-ASI Gambit [use template]

Chat 3: Analyze V-22 Osprey [use template]

Token Benefit Prompt caching (available in Projects) means repeated prompts aren't fully re-tokenized.

• Manual rewriting each time: Each prompt re-tokenized in full

• Template reuse in Projects: First use tokenizes template, subsequent uses get cached hit (90% cost reduction)

Creating Your Prompt Library

1. Identify 5–10 recurring tasks (analysis, drafting, coding, summarization)

2. Write a template for each with [VARIABLE] placeholders

3. Store templates in a Project-level document

4. Reuse same template structure across similar tasks

Template Examples

• Technical analysis (systems, weapons, platforms)

• Policy briefs (problem, current approach, alternatives, recommendation)

• Code review (architecture, security, performance, maintainability)

• Content drafting (outline, research questions, audience, tone)

---

Technique 12: Keep Personal Context Under 2,000 Words

The Problem A 20,000-word personal context file loads into every single conversation. That's 20K tokens of overhead before you type your first question.

Real Impact A 20K-word context file in a 200K limit:

• 10% of your token budget consumed by context alone

• Every conversation starts 20K tokens in the hole

• A 1-hour work session might be 50% context + 50% actual work

Example: Bloated Context

[USER PROFILE: 20,000 words covering]

- Entire work history (every job description)

- Complete family tree and relationships

- Full list of 50+ projects and their outcomes

- Every skill and certification

- All medical history and preferences

- Complete reading list and book summaries

- Full financial situation

- Detailed hobby list

[END: 20,000 tokens burned before starting]

The Trimmed Version

[USER PROFILE: 1,500 words covering]

- Current role: Retired Senior Engineer, radar systems

- Key expertise: Signal processing, C4ISR, AMASS

- Current projects: IPCSG advocacy, technical writing

- Key context for Claude: Prostate cancer patient-advocate, 

  uses pseudonym "Pseudo Publius" for civic policy work

- Preferences: HTML for newsletters, Markdown for analysis

[END: 1,500 tokens used for genuine context]

What to Keep in Context (1,500 words max)

• Current professional role

• 3–5 core skills Claude needs to know about

• 1–2 active projects

• Key preferences (format, tone, communication style)

• Any ongoing work Claude should reference

What to Cut

• Complete work history (mention only current role)

• Family relationships unless directly relevant

• Completed projects (list only active ones)

• Medical details beyond "patient advocate in X field"

• Reading lists, hobby catalogs, exhaustive skill inventories

• Historical context that isn't actively shaping current work

How to Structure Trimmed Context

# Claude Context (Keep Under 2,000 Words)

## Professional

- Role: Retired radar systems engineer, 20+ years

- Current focus: Technical writing, IPCSG patient advocacy

- Key expertise: Signal processing, SAR/GMTI, C4ISR systems

## Active Projects

1. IPCSG newsletter (prostate cancer research translation)

2. Naval Institute-style technical analysis (defense systems)

3. San Diego civic policy research (transit, water, governance)

## Preferences for Claude

- HTML output for IPCSG content (avoid .docx)

- Markdown for technical analysis

- Cite sources for health/policy content

- Flag when content needs fact-checking

## Key Context

- Patient with 11+ years prostate cancer history

- Uses "Pseudo Publius" pseudonym for civic policy writing

- Lives in San Diego, familiar with local transit/healthcare systems

- Enrolled in CONVERGE-01 actinium-225 PSMA trial at UCSD

Token Savings

• 20K context file: 20,000 tokens per conversation

• 2K context file: 2,000 tokens per conversation

• 10 conversations/week: 180,000 token savings per week

• Monthly savings: 720,000 tokens (enough for 3-4 complex analysis projects)

---

13. Leverage Conversation Search Before Asking

The Problem You ask Claude a question you've already solved in a previous chat. Claude re-answers from scratch, consuming tokens for work already done.

Solution Use the conversation search tool to find past relevant chats before messaging Claude. If you find the answer, you're done (zero tokens). If not, you have context for a more targeted question.

Example Instead of: "How do I configure AMASS for multi-sensor fusion?" Search first. If found in past chat, copy the answer. If not found, ask: "I've searched my past work—it's not there. Here's what I tried last time [specific detail]. What's the next step?"

Token Cost

• Re-asking and re-answering: 2,000–3,000 tokens
• Searching + targeted follow-up: 500 tokens
• Savings: 67–75%

---

14. Use Structured Output Formats to Reduce Back-and-Forth

The Problem You ask a question, Claude gives prose, you ask for it in table format, Claude reformats. Two exchanges for one deliverable.

Solution Specify the exact output format upfront: "JSON with keys: name, value, unit" or "Markdown table: columns are X, Y, Z" or "CSV format" or "Numbered list with 1-sentence descriptions."

Example

• Inefficient: "List the key features of the MQ-9B"

  • Claude responds with prose paragraph
  • You: "Can you make that a table?"
  • Claude reformats (2 exchanges, 4,000+ tokens)

• Efficient: "List MQ-9B key features as a markdown table with columns: Feature, Specification, Operational Impact"

  • Claude responds with table in one go (1 exchange, 1,500 tokens)

Token Savings: 60%

---

15. Use Claude's "Drafts" or Internal Reasoning to Reduce Revisions

The Problem You ask Claude to write something, it's close but needs tweaks, you ask for revision, Claude rewrites. One task becomes three messages.

Solution In the initial prompt, ask Claude to "show your thinking first" or "provide a draft + notes on what could improve it." Claude self-critiques, reducing revision cycles.

Example

• Inefficient: "Write a technical brief on the Constellation-class frigate cancellation"

  • Claude writes
  • You: "It's good but needs more detail on cost overruns"
  • Claude revises (2 exchanges minimum)

• Efficient: "Write a technical brief on the Constellation-class frigate cancellation. Include: executive summary, cost breakdown, timeline of delays, political context. Flag any sections that feel weak or incomplete."

  • Claude writes with self-critique built in
  • You get a more complete product on first try (1 exchange)

Token Savings: 40–50% (fewer revision cycles)

---

16. Reuse Outputs as Inputs (Chaining Without Re-Prompting)

The Problem You ask Claude to analyze data, then ask it to write a summary of that analysis. Claude re-reads both the original data and its analysis.

Solution When Claude produces output you'll use as input for another task, say so upfront: "Analyze this data, then use your analysis to draft a one-paragraph summary."

This chains tasks in a single message, avoiding re-reads.

Example

• Inefficient:

  • Message 1: "Analyze Q3 sales by region" (Claude analyzes)
  • Message 2: "Summarize that analysis for a board memo" (Claude re-reads data + analysis)
  • (2,500 + 2,500 = 5,000 tokens)

• Efficient:

  • Message 1: "Analyze Q3 sales by region, then summarize findings in one paragraph for a board memo"
  • (3,500 tokens, single pass)

Token Savings: 30%

---

17. Specify Constraint Limits Upfront to Avoid Scope Creep

The Problem You ask for "an analysis," Claude writes 2,000 words because no constraint was given. You then ask "can you make it shorter?" Claude re-writes. Wasted tokens.

Solution Always specify: word count, depth level, or section count upfront.

Examples

• "Summarize in 3 sentences"
• "Brief analysis (under 500 words)"
• "Outline only—no prose, just 5 bullet points per section"
• "Executive summary format: 1 page max"

Token Impact

• Unconstrained ask: 3,500 tokens → You request trim → 2,000 tokens for re-work (5,500 total)
• Constrained ask: 1,500 tokens (right size on first try)
• Savings: 73%

---

18. Cache Repeated Context by Using "Assume You Know" Statements

The Problem Every time you chat, you re-explain your domain, your current project, or your constraints.

Solution Once you've established context in a chat, use "assume you know" statements to avoid re-explaining:

• "Assume you know the San Diego MTS budget structure from our earlier discussion"
• "Assume you're familiar with the CONVERGE-01 trial protocol"
• "Assume you know the PIRAN radiation belt software context"

This signals Claude to reference prior exchanges without restating everything.

Token Cost

• Restating context every time: 800+ tokens per message
• Using "assume" reference: 200 tokens per message
• Savings: 75%

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19. Use Negative Constraints (What NOT to Include)

The Problem Specifying what you want is harder than specifying what you don't. "Don't explain basic concepts I already know" saves tokens better than "explain advanced concepts."

Solution Frame prompts with what to exclude:

• "Analyze this without explaining what a PSMA scan is"
• "Write the technical section without introductory material"
• "List only the novel findings—skip anything in standard literature"

Example

• Inefficient: "Explain the latest prostate cancer biomarkers" (Claude might explain what biomarkers are, burning tokens on known info)
• Efficient: "Explain novel prostate cancer biomarkers, assuming I know what biomarkers are and how standard testing works"

Token Savings: 20–30%

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20. Compress Intermediate Outputs via Summarization Prompts

The Problem You ask Claude to do a deep analysis (3,000 tokens), then ask questions about it. Claude must re-read the full analysis for each question.

Solution After the analysis, immediately ask Claude to produce a "compressed summary for reference." You then use the summary for follow-ups, not the full analysis.

Example

• Message 1: "Analyze the 50-page NTSB docket on the LaGuardia collision" (3,000 tokens)
• Message 2: "Compress that into a 10-point summary I can reference for follow-ups" (500 tokens)
• Messages 3+: Ask questions referencing the summary, not the original analysis (saves 2,000+ tokens per follow-up)

Token Impact

• With compression: Original (3,000) + summary (500) + 5 follow-ups using summary (2,500) = 6,000 total
• Without compression: Original (3,000) + 5 follow-ups re-reading full analysis (15,000) = 18,000 total
• Savings: 67%

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21. Use Pseudocode or Outline Mode for Complex Tasks

The Problem You ask Claude to solve a complex problem in full detail. It writes long explanations. You ask for just the outline. It re-writes.

Solution Ask for "pseudocode" or "outline mode" first, then expand only sections you need.

Example

• Inefficient: "Help me design a system for analyzing satellite megaconstellation fragmentation" (Claude writes 2,000-word design doc)
• Efficient:
  • Message 1: "Outline only: system architecture for analyzing satellite megaconstellation fragmentation" (Claude: 300-word outline)
  • Message 2: "Expand section 3 (data pipeline) to full technical detail"
  • (1,000 + 1,500 = 2,500 vs 2,000 + potential revisions)

Token Savings: 20–40% (you pay only for sections you need)

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22. Pre-Process Data Externally Before Uploading

The Problem You upload raw messy data (10K tokens), Claude cleans it, then you ask questions. Claude must re-read the messy + cleaned data.

Solution Clean/process data before uploading. Use a spreadsheet tool, Python script, or other lightweight processing first.

Example

• Inefficient: Upload 500-row CSV with duplicates, formatting issues, irrelevant columns (8,000 tokens) → Claude cleans and analyzes
• Efficient: Clean in Excel/Python locally (2 min, no tokens) → Upload cleaned 200-row CSV (2,000 tokens) → Claude analyzes

Token Savings: 60–75%

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23. Use Checkpoints: "Are We On Track?" Mid-Conversation

The Problem You work through a complex analysis with Claude, go down the wrong path for 10 messages, then realize the approach is wrong. All 10 messages must be re-read going forward.

Solution Every 5–7 messages in complex tasks, insert a checkpoint: "Summarize progress so far and confirm we're on the right track before continuing."

If wrong track, you catch it early. If right track, you've created a compressed summary for future reference.

Token Cost

• Wrong path after 10 messages: Wasted 6,000 tokens, plus future re-reads (12,000 total over conversation)
• Checkpoint at message 5: Catch early, save 10 messages of wasted work (10,000 tokens)
• Savings: 83%

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24. Leverage Templates with Conditional Sections

The Problem (Similar to #11, but more sophisticated) You have a template, but different uses require different sections. You still re-write parts.

Solution Build templates with conditional markers. Example:

# Technical Analysis Template

## Executive Summary (always)
[1 paragraph]

## [IF: System is military] Operational History
[relevant section]

## [IF: System has sensors] Sensor Capabilities
[relevant section]

## Key Metrics (always)
[data table]

## [IF: System is controversial] Safety/Incident History
[relevant section]

When reusing, you fill only the sections relevant to the specific system.

Token Benefit

• Manual rewriting each time: 100% re-tokenization
• Template with conditionals: Reusable frame (cached) + conditional sections only
• Savings: 40–50% on repeated similar analyses

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25. Use "Status Check" Outputs for Ongoing Projects

The Problem You're working on a multi-week project. Each new chat, you brief Claude on what's been done. That briefing is always ~1,000 tokens.

Solution At the end of each session, ask Claude to generate a "project status summary" (500 words). Start next chat by pasting that summary instead of re-explaining.

Example After session 1 on IPCSG newsletter research:

• You: "Create a 300-word status summary for my next session: what we've covered, what's pending, open questions"
• Claude: [Status summary] (800 tokens)

Next session:

• You: "Here's the status from last session: [paste]. Continue with the next section on ADT cardiovascular risk"
• Claude: (Uses summary, no re-explanation needed)

Token Savings

• Re-explaining each session: 1,000 tokens/session × 10 sessions = 10,000 tokens
• Status summary approach: 800 + 200×10 = 2,800 tokens
• Savings: 72%

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26. Batch Similar Queries to Use Prompt Caching

The Problem You ask 10 different questions about the same system (MQ-9B). Each question re-reads the full context.

Solution In Projects, ask all related questions about the same system in one session before moving to a new system. Prompt caching means the context gets tokenized once, reused for all questions.

Example

• Chat 1: "Answer all questions about MQ-9B SeaGuardian" + [list 10 questions]
  • Questions about same context leverage caching
• Chat 2 (different day, same project): Ask 10 questions about Gambit CCA
  • New context, but again leveraging caching within session

Token Benefit

• Separate chats for each question: 10 questions × 2,000 tokens = 20,000 tokens
• Batched in one chat with caching: 2,000 (context) + 500 (questions) = 2,500 tokens
• Savings: 87.5%

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Summary: All 26 Techniques by Impact

Highest Impact (40%+ savings each)

• #1: Replace PDFs with markdown (85–90%)
• #7: Use Projects to avoid redundant uploads (80%)
• #3: Batch tasks (56%)
• #12: Trim personal context (70% when compounded)
• #20: Compress intermediate outputs (67%)
• #26: Batch similar queries with caching (87.5%)

High Impact (25–40% savings)

• #2: Right-size models (50% when applied systematically)
• #6: Short prompts (33%)
• #10: Crop screenshots (96% but narrow use case)
• #15: Show thinking first (40–50%)
• #17: Specify constraints upfront (73%)
• #23: Checkpoints (83%)

Medium Impact (15–25% savings)

• #4: Edit instead of stacking (60% but single-message impact)
• #5: New chats for new topics (40% for multi-topic conversations)
• #8: Disable tools (varies by tool usage)
• #9: Restart conversations (55% but only if you hit 50+ messages)
• #13: Search before asking (67% but only if found)
• #14: Specify output format (60% but narrow use case)
• #16: Chain tasks (30%)
• #18: "Assume you know" statements (75% but only after context established)
• #19: Negative constraints (20–30%)
• #21: Pseudocode mode (20–40%)
• #22: Pre-process data (60–75% but narrow use case)
• #24: Conditional templates (40–50%)
• #25: Status summaries (72%)

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The Strategic Layer: What Most People Miss

Beyond these 26 techniques, there's one meta-insight:

Token efficiency is a systems problem, not a tips-and-tricks problem.

Most users treat Claude like a search engine: ask question, get answer, move on. That model inherently wastes tokens because there's no continuity.

Efficient users treat Claude like a long-term collaborator:

• One Project per major body of work (IPCSG research, Naval analysis, San Diego civic work)
• Persistent templates, reusable context, shared files
• Conversations that build on each other (status summaries, checkpoints)
• Clear handoffs between sessions (summary → next session → summary)

When you operate at the "system" level instead of the "single query" level, all 26 techniques compound. You're not just saving tokens on individual exchanges—you're building workflows that stay efficient across months.

That's the real win.

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Integrated Workflow: Putting It All Together

Here's how these 12 techniques work together in practice:

Scenario: Research and Write a Technical Analysis

The Bloated Approach

1. Upload raw 15-page PDF (30,000 tokens)

2. Write 300-word prompt with every detail (prompt re-read 15+ times)

3. Send message → realize you need more info → "Actually, I meant…" 

   (correction stacking)

4. Ask 3 follow-ups in separate messages (context re-read x3)

5. Use Opus for summarization (wrong model)

6. Keep web search enabled (unused, 300 token overhead)

7. Two weeks later, use same PDF in new chat (30,000 tokens again)

8. Maintain 35-message conversation (10,000 tokens re-read overhead)

Total waste: ~95,000 tokens

The Optimized Approach

1. Project: "Technical Analysis" (upload PDF once as .md)

   [3,000 tokens vs 30,000]

2. Short prompt (25 words, edited before sending)

   "Analyze MQ-9B SeaGuardian: range, sensors, naval integration, 

    format as summary + technical breakdown + comparison table"

   [Prompt re-read cost: minimal]

3. Batch all questions into one message, use Sonnet (not Opus)

   [1/3 the cost, 90% as good]

4. Disable web search and tools (enable only if needed)

   [No overhead]

5. Reuse prompt template for future system analyses

   [Prompt caching reduces repeat cost 90%]

6. Keep conversation to 18 messages, then restart

   [Minimal re-read overhead]

Total usage: ~10,000 tokens

Savings: ~85,000 tokens (90% reduction)

Scenario: Customer Support Email + Dataset Analysis in One Session

Wrong: One chat with both tasks, tools enabled, Opus for both

Right:

Chat 1: "Draft customer support email"

- Task: Simple templating

- Model: Haiku

- Tools: Off

- Expected: 1-2 messages

Chat 2: In same Project, "Analyze Q3 customer data"

- Task: Statistical analysis

- Model: Sonnet

- Tools: Off (already have data)

- Expected: 3-4 messages

[Both reference same Project files, no redundant uploads]

[Different tasks, different chats, minimal re-reading]

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The Economics: Real Savings

For Individual Users

• Using all 12 techniques: ~60–70% token efficiency improvement

• A 200K limit effectively becomes ~300K in actual work capacity

• Cost savings: If paying per token, 30–40% reduction in bills

For Teams (10 users)

• Typical: 500 chats/month, 50M tokens burned

• Optimized: 500 chats/month, 15M tokens used

• Monthly savings: 35M tokens

• Billable value: Equivalent to ~$5,000–$10,000/month in unused capacity recovered

For Enterprises

• Bloated workflows lead to:

• Team members buying extra credits (hidden costs)

• Unnecessary token quota expansions

• Perceived "slowness" (actually just inefficiency)

• Optimized workflow means:

• Planned budgets actually cover work

• Clear ROI on Claude investment

• Scalability without proportional cost increase

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Implementation: Start Here

You don't need to adopt all 12 techniques simultaneously. Phase them in:

Week 1: Quick Wins (Saves ~30%)

• Technique 2: Right-size models (Haiku for simple tasks)

• Technique 6: Write shorter prompts

• Technique 10: Crop screenshots

Week 2: Process Changes (Saves additional 20%)

• Technique 3: Batch tasks into single messages

• Technique 5: Use separate chats for different topics

• Technique 4: Edit instead of stacking corrections

Week 3: Structural Optimization (Saves additional 25%)

• Technique 1: Replace PDFs with markdown

• Technique 7: Move files to Projects

• Technique 12: Trim personal context

Week 4: Advanced Optimization (Saves additional 15%)

• Technique 8: Disable tools by default

• Technique 11: Build prompt templates

• Technique 9: Restart conversations every 15–20 messages

Expected Result After 4 Weeks: 70–80% improvement in token efficiency

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The Mindset Shift

Token optimization isn't about deprivation—it's about clarity. When you're forced to communicate concisely, write better prompts, and focus on one task at a time, you get better results and use fewer tokens.

Every inefficient workflow pattern masks itself as "flexibility" or "exploratory thinking." In reality, it's just waste.

The 12 techniques above are the proven guardrails. Use them, and you'll never feel constrained by Claude's limits again. The constraint becomes a feature: it forces you to think like an engineer, not just an experimenter.

Your next Claude session will be 3x more productive and cost 70% less.