Ukraine and Iran have proven that cheap autonomous mass changes warfare, but the platforms only work because the data underneath them does. The U.S. is losing ground on both fronts: procurement built for exquisite systems cannot deliver cheap-at-scale, and a force built around data collection has never solved data interpretation. The next advantage will come from being able to act on the data we’ve already collected.
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There’s been a lot of news coverage about Iran's use of low-cost Shahed drones against U.S. and partner air defenses.
Nearly all of it is wholly fixated on the cost imbalance.
For context: a Shahed-136 runs somewhere between $20,000 and $50,000 per unit, depending on whose teardown you trust. A Patriot PAC-3 interceptor used to knock one down is north of $3 million.
That math is real…but also a distraction.
Cost asymmetry is a symptom. The old equation said precision and mass were tradeoffs. Exquisite systems delivered precision. Mass came from cheaper, less accurate platforms. You picked one.
That equation no longer holds.
Ukraine has spent four years showing us what this new operating model of warfare looks like.
Low-cost autonomous systems can now be fielded in volume across air, sea, and ground domains while still hitting specific targets with meaningful accuracy. The advantage shifts from who has the most capable single platform to who can generate volume, coordinate distributed effects, and adapt fastest.
Drones now account for the majority of battlefield effects in some sectors of that fight. Both sides are deploying thousands of small unmanned systems daily.
The clearest demonstration of the new operating model came last June.
In Operation Spider's Web, Ukraine's SBU smuggled 117 FPV drones into Russia hidden inside cargo trucks and struck five air bases simultaneously, including targets more than 4,000 kilometers from the Ukrainian border. The Financial Times put the loss at roughly 20% of Russia's operational long-range aviation fleet. Many of those airframes have not been produced since the Soviet Union dissolved and cannot be replaced.
What gets less attention is what made Spider's Web possible in the first place:
The drones were the delivery mechanism. The targeting picture was the actual weapon, and it was assembled from data sources that were available to anyone willing to fuse them into something operators could act on.
That is the part of what the cost-asymmetry conversation keeps missing. Cheap mass without a clear targeting picture is just noise. Cheap mass paired with the right data, fused fast enough to matter, is what put $7 billion of strategic aviation on a runway.
The Shahed-136 program reflects three deliberate choices: prioritize cost imposition, trade precision for volume, and build a strategy around exhausting high-end defenses.
According to CSIS analysts, that is exactly what the platform was designed to do: saturate radars, force command centers to spend expensive interceptors on cheap drones, and create attack windows for higher-end munitions to get through.
What the Iran case confirms is that this is not a quirk in Ukraine. Rather, we need to treat this as the new baseline.
We are about to walk straight into a second-level problem most of the conversation is ignoring entirely: a data environment no current command structure is built to interpret.
Cheap drones, off-the-shelf cellular networks, civilian trucks, and AI-assisted terminal guidance erased a strategic bomber fleet. That should have been the wake-up call.
Here is the part that has stayed with me through every conversation I have had with operators over the past year, including at the most recent USSOCOM Tech Experimentation event focused on agentic AI: we are reading the same intelligence Iran is reading.
We are watching the same Ukraine footage. The lessons are not hidden. We are losing ground because the procurement system was built for a different war.
The Replicator timeline is the cleanest example.
The reasons aren’t exactly mysterious. Defense acquisition culture remains anchored in a 1960s paradigm built around centrally-planned, linear, highly predictive procurement of expensive systems with long lifecycles.
That culture is incompatible with cheap-at-scale. Meaning we’re now operating in a world where the platform is supposed to be expendable, the design iterates every few months, and the production line needs to be measured in tens of thousands per year, not dozens.
Based on other reports, a Ukrainian interceptor drone costs around $15,000. A comparable U.S. system costs roughly ten times that. The gap is not so much the technology as it is the requirements process, the test-and-evaluation timeline, and an outdated contracting system that rewards platforms which exist for decades over platforms which are designed to be consumed in weeks.
Iran is applying lessons from Ukraine faster than the U.S. defense enterprise is institutionalizing them, all while using their data to make the most of these platforms. That’s the gap I want every defense leader to sit with for a minute.
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The conversation usually stops with a call for faster procurement and more drones. Both are necessary while neither is sufficient.
The bigger structural change is that power projection is decentralizing. Not gradually. Structurally.
Our adversaries are not out-buying or outmuscling us on platforms. They are out-integrating us on data, and the platforms only matter because the data underneath them does.
The platforms get smaller and more numerous while decisions get harder.
Because the people pushing harder on the platform side keep underweighting one thing: the operating model only works if you can actually understand what the distributed network is telling you in time to act on it.
The further we push toward decentralized power projection, the more of the force becomes data-generating, and the more the actual fight comes down to whether anyone on our side can pull a coherent picture out of what is being collected.
Every autonomous platform produces telemetry, sensor data, behavioral data, and environmental context.
Every Shahed launch Iran orders is informed by a targeting picture pulled from commercial imagery, regional ISR, and open-source feeds, assembled into something operationally useful. Every Ukrainian strike deep into Russia depends on the same kind of fusion work.
Now scale that globally. Swarms of aerial drones, distributed maritime systems, tactical ground sensors. commercial ISR and open-source feeds. The "transparent battlefield" the Army has been writing about is no longer aspirational.
It is emergent, and it is producing a class of data problems the U.S. defense enterprise has spent decades creating without ever solving.
The issue is not collection. We are world-class data collection addicts. The issue is correlation, interpretation, and decision-readiness. A battlefield where everything is visible is only useful if it is understandable.
This is the gap I see every time I talk to soldiers, sailors, and operators:
The next real advantage will not come from another drone, another sensor, or another data source. It will come from the ability to turn all of that into something a human can actually use in real time.
This is the thesis we built Certus Core's IBIS™ platform around. Integrate heterogeneous data sources. Map them to mission context. Deliver decision-ready outputs through plain-language queries. Not another dashboard. A translation layer for the distributed force that decentralized power projection is creating whether we are ready for it or not.
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If we want to close the gap with adversaries who are operationalizing these lessons faster than we are, the work splits into three parallel tracks.
Fix the procurement chokepoints that are slowing autonomous mass to a trickle. Replicator's troubles are not a reason to abandon the model. They are a reason to dismantle the requirements and test cycles that produced them.
Treat data integration as a core warfighting capability, not an IT problem. Every dollar spent on another sensor without a corresponding investment in correlation and interpretation is a dollar spent on widening the gap between what we can see and what we can decide.
Operators closest to the mission have the context, so move the decision to them versus the other way around. They need tools that let them query distributed data the way they think, not the way the underlying databases store it.
The side that wins the next fight will not just have better platforms. It will have a better understanding of what all those platforms are telling them, and the ability to act on that understanding faster than anyone else.
See how chat-based queries + mission-derived context + AI governance eliminates the tradeoff between speed and accuracy with IBIS™.
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