Mandated by President Trump, this acceleration strategy will unleash
experimentation, eliminate legacy bureaucratic blockers, and integrate
the bleeding edge of frontier AI capabilities across every mission area
to usher in an unprecedented era of American military AI dominance.
"We will unleash experimentation, eliminate bureaucratic barriers,
focus our investments and demonstrate the execution approach needed to
ensure we lead in military AI," said Secretary of War Pete Hegseth. "We
will become an 'AI-first' warfighting force across all domains."
The Department is taking a wartime approach to delivering
capabilities, with an emphasis on three tenets: warfighting,
intelligence and enterprise operations. This approach will strengthen
battlefield decision-making, rapidly convert intelligence data and
modernize daily workflows, all in direct support of more than three
million DoW personnel.
The catalyst for this acceleration will be seven Pace-Setting
Projects (PSPs), [...]
Warfighting
Swarm Forge: Competitive mechanism to iteratively
discover, test, and scale novel ways of fighting with and against
AI-enabled capabilities – combining America's elite warfighting units
with elite technology innovators.
Agent Network: Unleashing AI agent development and
experimentation for AI-enabled battle management and decision support,
from campaign planning to kill chain execution.
Ender's Foundry: Accelerating AI-enabled
simulation capabilities - and sim-dev and sim-ops feedback loops - to
ensure we stay ahead of AI-enabled adversaries.
Intelligence
Open Arsenal: Accelerating the TechINT-to-capability development pipeline, turning intel into weapons in hours, not years.
Project Grant: Enabling transformation of deterrence from static postures and speculation to dynamic pressure with interpretable results.
Enterprise
GenAI.mil: Providing Department-wide access to
frontier generative AI models, like Google's Gemini and xAI's Grok, for
all DoW personnel at Impact Level (IL-5) and above classification
levels.
Enterprise Agents: Building the playbook for rapid and secure AI agent development and deployment to transform enterprise workflows.
This AI Acceleration Strategy is driving a major expansion of AI
compute infrastructure through targeted investments and will unlock
access to the data that gives the War Department an asymmetric edge. The
Department will bring in top American AI talent through initiatives
like the Office of Personnel Management's "Tech Force" initiative and
will empower small, accountable teams to attack complex AI integration
opportunities. The War Department will eradicate woke DEI from our AI
capabilities and ensure our military has objective, mission‑first
systems that will guarantee decision superiority and warfighting
advantage in this AI era.
An assymmetric edge, free of woke. A few good men and all that. A promise for a War Department future, where DOD is as yesterday as vinyl records. And no woke, in case you missed that. Testosterone rules, Hegseth on down.
An important paragraph at the end -
"Speed defines victory in the AI era, and the War Department will
match the velocity of America's AI industry," said Emil Michael, Under
Secretary of War for Research and Engineering. "We're pulling in the
best talent, the most cutting‑edge technology, and embedding the top
frontier AI models into the workforce — all at a rapid wartime pace."
Speed. When NFL quarterbacks are evaluated, a quick release matters, but accuracy matters more. As in don't target a girls school, in the "warfighter" context.
Grounded in the core tenets of warfighting, intelligence and
enterprise operations – and following President Trump's direction – the
War Department will accelerate America's Military AI Dominance by
becoming an AI-first warfighting force across all domains.
GAMBIT SERIES - Hoo Rah. And Anthropic wants a guardrail upon the hallucinating tendency of AI when done via LLM deep neural nets which can from time to time show up. Reliability worry. Girls school and all that. Targeting faster than human oversight can oversee. The new War Department.
We have the strength and power to do it alone, but not exactly.
The boat goes faster the more oars being pulled? What?
AI, drones, and advanced sensors have changed the tempo of modern
warfare. Yet acquisition cycles, certification timelines, and training
pipelines still operate at industrial-era speed.
AI — particularly at the edge in denied, disrupted, intermittent, and
limited environments — is now central to operational resilience and
real-time decision-making.
But most programs continue to struggle with time-to-deployment. On
the battlefield, “almost ready” is indistinguishable from not ready.
Marginal performance gains rarely change outcomes if the model
arrives too late, cannot adapt to changing conditions, or fails on
deployed hardware. A 1 percent accuracy improvement does not compensate
for a six-month update cycle.
US
Army soldiers inspect a small counter-drone system during Project
Flytrap 4.5 testing in Germany. Photo: Staff Sgt. Yesenia Cadavid/DVIDS
Stalling Between Prototype and Deployment
Most AI failures are not caused by a lack of ideas. They occur in the
transition from promising demo to operational system, where
experimentation meets doctrine, acquisition, and real-world constraints.
A few failure patterns appear repeatedly.
Linear development processes are applied to adaptive systems.
Sequential handoffs slow iteration and blunt the feedback loop from
operators.
Validation cycles are misaligned with operational tempo. Lengthy review timelines conflict with rapidly evolving mission needs.
Compliance frameworks built for static software struggle with
continuously evolving models. One-time certification does not fit
systems that must update frequently to remain relevant.
Technical barriers compound the issue. Hardware diversity across
platforms forces re-optimization and re-engineering. Models tuned for
one compute environment often fail to translate to another. Fragile
lab-to-field pipelines rely on manual integration and bespoke
configurations, slowing repeatability and scale.
Operational fragmentation is equally limiting. Developers, operators,
and acquisition teams operate under different incentives and timelines.
No single authority is accountable for time-to-field. Past program
failures reinforce risk aversion, encouraging extended experimentation
over operational commitment.
The result is predictable: pilots proliferate, deployments stall.
Compressing AI Deployment Timelines
Reducing deployment time requires treating fielding as a primary design requirement, not a downstream phase.
Portability across compute environments must be engineered from the
start, enabling models to run across edge, tactical, and centralized
systems without extensive rework.
Leaders must also embrace simplicity as a design principle. Fewer
dependencies reduce integration time, while predictable behavior
accelerates trust and adoption.
Development, testing, and deployment must function as a continuous
lifecycle. Automated pipelines replace manual integration. Models are
built with field constraints in mind, and transition planning begins
early rather than after a successful demo.
Governance must evolve as well. Static certification models should
shift toward lifecycle oversight — monitoring performance, risk, and
reliability continuously. Controls adjust based on operational context
rather than blocking iteration outright.
Critically, time-to-deployment should become a formal performance
metric alongside technical accuracy and cost. Programs should be
evaluated not only on what they build, but how quickly it reaches
operators.
AI must be considered an iterative process, setting a minimum
threshold to field it and iterate with field data to deliver the best
value.
Conceptual illustration of AI in the military. Photo: US Mission OSCE
Speed as Capability: Implications for Defense Leaders
The strategic consequence of slow AI deployment is straightforward: advantage shifts elsewhere.
In prolonged competition, the force that iterates faster shapes the
operational environment. Resilience depends on the ability to deploy,
update, and iterate continuously, not just to build once.
The next phase of military AI adoption requires two shifts.
First, move from experimentation to execution. Fewer isolated pilots,
more deployable capabilities, and clear transition paths to operational
use.
Second, embed speed into requirements and oversight. Delivery
velocity must be treated as mission-critical, not as a secondary
efficiency metric.
A recent US Navy implementation supporting Project AMMO illustrates the impact.
By restructuring the model update pipeline and reducing manual
integration steps, the time required to update an AI model dropped from
six months to a matter of days — a 97 percent reduction. That change did
not improve accuracy by a fraction of a percent. It changed how quickly
capability could adapt in the field.
Time-to-deployment is no longer a supporting concern. It shapes readiness, resilience, and deterrence.
In AI-enabled defense, velocity is strategy. The force that deploys first — and adapts fastest — holds the advantage.
Jags Kandasamy is CEO and Co-Founder of Latent AI.
Phrased otherwise, fuck you Anthropic, take your Claude and shove it, it's risk tainted, we say so, but wait a bit for us to find some other vendor with less inclination toward guardrails who can still interface with Palantir - our way. The only way.
(Ignore for now that nasty little massive domestic surrveilance qualifier, look elsewhere.)
Something like that, and for now Claude/Maven is being war tested, (the only test - warfigfhters fighting a war) so wait a bit to shove your Claude, okay?
That image is from here. Now we can speculate, from mid-item - about getting their minds right:
Google’s press release on the day of the Pentagon’s announcement said
that its model is designed to supply the DOD workforce with “an edge”
through natural language conversation and retrieval-augmented
generation.
RAG refers to a technique that essentially makes chatbots and other
models more reliable, by enabling them to look up information from a
specific set of relevant data sources before answering a question.
Prior Defense Department-supplied AI options rely on RAG datasets
that are created locally, or specifically shared with individual users
or user groups. This approach works well, according to a defense
official, because it allows people to deliberately curate datasets that
contain accurate information tailored to their specific purposes.
The official suggested that Google’s offering in the platform has an
internal dataset that appears to consist of cached, scraped web content
that is enabled by default. Google reportedly places citation markers
next to content pulled.
“So functionally, Google has effectively included an internal dataset
that — unless explicitly disabled — draws from web-based data sources.
The upshot, your ragheads should only rely on officially generated RAG? What? Or, don't call them ragheads, since they may regard that as an un-woke usage?
Call them warfighters, even at the remf jobs? (If remf is still an employed usage?)
