Greg Little is a senior counselor at Palantir Technologies.

Aaron Jaffe is a deployment strategist at Palantir Technologies.

A few years ago, one of us sat in a conference room late in the afternoon as a major weapon system program team debated a marginal performance improvement. Single-digit percentages. Decimal places. Over hours of discussion, what no one talked about was how many of these systems could be built, how fast they could be replaced, or whether the parts could be sustained at scale.

That meeting captures a pattern the Department of War knows too well: we design the ideal weapon first and only later ask whether the country can actually produce it in the quantities a prolonged fight would demand.

This is why Secretary of War Pete Hegseth’s recent acquisition transformation announcement matters. His focus on speed and scale, on shifting the department to a wartime footing, and on breaking a culture that rewards compliance over execution points us in the right direction. It acknowledges something fundamental: capability delivered late—or in boutique quantities—is not capability at all. But if we really want speed and scale, acquisition reform alone isn’t enough. The deeper problem isn’t how we buy weapons. It’s how we design them and coordinate their production.

Designing for Scale

For decades, the department has treated manufacturing and the industrial base as downstream concerns. Requirements are written as if time, production capacity, and supply availability are secondary. Only after the system is defined—sometimes after it’s certified—do we focus on scaling production. This approach produces impressive prototypes. It does not produce the mass and responsiveness required for deterrence at scale. If we want to field lots of weapons fast, we need to flip the logic: design weapons systems to fit the industrial base we have and then iterate—rather than designing unicorns and hoping the supply chain catches up.

World War II is often cited as proof that America can surge production when it matters. That’s true, but the reason we surged is often misunderstood. Yes, we mobilized labor. Yes, we built factories. But the real breakthrough wasn’t just industrial effort—it was design choices that embraced industrial reality.

Consider aircraft production. In 1940, the U.S. aircraft industry was small and craft-based, producing roughly 3,600 aircraft per year. By 1944, it had become the world’s largest aircraft producer, outproducing Germany and Japan combined—and fielding over 96,000 aircraft that year alone. This transformation didn’t come from engineers pursuing optimal designs. It came from designing aircraft around manufacturing capabilities first, with performance improvements emerging organically through production experience and operational feedback.

Within four years, the U.S. designed, introduced, and built more than 30 different bombers, iterating from the workhorse B-17 to the advanced B-29 with more than twice the power, range, and load. By comparison, the latest bomber produced in the United States, the B-2, required more than a decade to move from design to the initial production of a fleet of 21 aircraft.

America’s rapid wartime evolution was spurred by a collaboration between government and manufacturers to maximize the use of their existing capabilities. At Willow Run, Ford—the automobile company—adapted aircraft designs to the logic of automotive assembly, using standardized parts, simplified subassemblies, and repeatable processes. By 1944, a B-24 bomber was rolling off the line nearly every hour. Ford optimized for throughput, not perfection—and throughput won the war.

This story was repeated across the American industrial base. At the Detroit Arsenal, Chrysler applied the same logic to tank engine production. The Sherman tank wasn’t designed to win one-on-one duels against the most advanced enemy armor. It was designed to be built quickly in enormous quantities and to be easily repaired in the field. The result was a tank that could be produced and replaced faster than adversaries could destroy it.

This design-for-scale philosophy is not a complete relic of World War II—it’s re-emerging today where speed and volume matter most. Anduril’s Barracuda cruise missile is a modern example of this same logic. Rather than designing a missile optimized solely for performance and then struggling to manufacture it, Barracuda was designed from the outset to be compatible with high-volume production, using components, processes, and tolerances that allow it to be built on current automotive manufacturing capabilities. The result is a system explicitly designed for mass production, rapid replenishment, and iterative improvement.

This is an entirely different mindset for manufacturing, and it has strategic consequences for the nations that embrace it. Production reality becomes the key design constraint, not an afterthought. Interchangeability isn’t just a maintenance convenience—it’s a warfighting advantage. Designs that can tolerate variation and substitution proliferate. Designs that can’t are redesigned or abandoned. The lesson is simple and uncomfortable: the United States doesn’t scale by asking industry to stretch to meet perfect designs. It scales by aligning designs to industrial strength.

Here’s where this gets uncomfortable. If a modern weapon system depends on a bespoke material, a limited supply base, or a certification regime that freezes design for a decade, then it may be technologically impressive—but it is strategically brittle. In a contested industrial war, elegance is not resilience. A system that cannot tolerate supplier substitution, component swaps, or rapid redesign is not “high-end.” It is pre-failed the moment production is disrupted or losses exceed peacetime assumptions.

We often describe these bad outcomes as manufacturing problems or supply-chain shocks. And yes, we need investment to expand industrial capacity and deepen critical supply chains. But at root these are design decisions—made early, reinforced by incentives, and rarely revisited. And then we act surprised when scale never materializes.

Orchestrating Production

Too many in the Department of War still operate on the assumption that if we simply fix acquisition policy, streamline contracts, and empower program offices, the rest of the system will somehow align. It won’t, and Secretary Hegseth’s acquisition transformation recognizes this. Even if every acquisition reform memo is executed perfectly, the department is still missing something fundamental: a way to operate the war machine as a single, connected system.

Today, weapon design lives in one world. Manufacturing capacity lives in another. Supply chains live in spreadsheets. Sustainment data arrives late. Readiness has little connection to its industrial inputs. Learning happens—but slower than the fight evolves. This is a coordination problem.

What’s missing is a war machine operating system—not a product, but a capability. A digital layer that connects weapon design decisions, factory throughput, supplier health, substitution options, and sustainment realities into one continuously updating picture.

There is precedent for this idea, and it comes from an era long before software existed. During World War II, the United States effectively created a human version of a war machine operating system. Through institutions like the War Production Board—and figures like William S. Knudsen, a former auto executive elevated to general officer and placed in charge of industrial production—the government unified design, manufacturing, supply chains, and battlefield demand under a single coordinating authority. Knudsen didn’t just manage contracts. He managed tradeoffs. He could simplify designs, redirect factories, force standardization, and prioritize throughput over elegance. He had visibility into what industry could produce and the authority to shape what was being designed accordingly. Knudsen wasn’t merely a bureaucrat. He was the conductor of a vast industrial orchestra.

What’s different today is that this coordinating function no longer has to live in a handful of individuals or emergency boards. It can live in software. In limited cases, it already does. During recent Navy ShipOS pilot deployments, AI-powered manufacturing and sustainment capabilities demonstrated what a war footing looks like in practice. At General Dynamics Electric Boat, submarine schedule planning was slashed from roughly 160 manual hours to fewer than 10 minutes. At Portsmouth Naval Shipyard, material review timelines that once took weeks were compressed to less than an hour. These changes show how a wartime industrial system moves when information, authority, and execution align.

The same model is already visible in commercial manufacturing enterprises that must operate continuously, at scale, and under real-world stress. Airbus’s Skywise platform connects design data, production systems, maintenance records, and operational telemetry across thousands of aircraft—creating continuous feedback between engineering, production, and operations. This is how modern industrial systems function when downtime, delay, and uncertainty are unacceptable.

War Footing

The key takeaway for the Pentagon is that operating a complex industrial machine at scale on a war footing is already possible. The limiting factor is adoption, integration, and leadership. Today, those feedback loops are fractured in the defense industrial base. Requirements, engineering, production, logistics, and sustainment are treated as sequential phases instead of one living system. By the time a problem surfaces, the opportunity to adapt cheaply has already passed. Now that the Pentagon is taking a war footing mindset and approach, it needs an operational backbone that treats design, production, and sustainment as one system, not as disconnected handoffs. Without this connective tissue, the department is forced to manage by lagging indicators and intuition—exactly the opposite of what a wartime footing demands.

Secretary Hegseth’s push for speed and performance is necessary—but it will only deliver results if the department changes what it rewards upstream. Designing for substitution, modularity, and common components is a strategic choice. It’s how you stay in the fight when conditions change—and they always do.

So here’s the rule leaders should apply before approving the next major weapon system: can this weapon be built, repaired, and replenished faster than it will be destroyed? If the answer is no, no amount of acquisition reform, contracting flexibility, or urgency memos will save it. In a fight defined by attrition, adaptation, and industrial endurance, the winning systems will not be the perfect ones on paper but the ones that can be produced, replaced, and improved the fastest.

America didn’t win past wars by hunting unicorns. We won by designing for scale—and then scaling relentlessly.

Greg Little is a senior counselor at Palantir Technologies.

Brighton Timmins is chief of staff of CHAOS Industries.

The government’s most important scorecard for contractor performance doesn’t actually keep score.

Imagine a contractor that goes above and beyond—that delivers ahead of schedule, under budget, and adds innovations that save the government millions. In the Contractor Performance Assessment Reporting System (CPARS), that contractor’s record looks nearly identical to a contractor that barely met the minimum requirements. Both are labeled “Very Good.” Both are safe from challenge. One outperformed, the other coasted, but the system treats them the same. That’s the problem: CPARS treats excellence the same as mediocrity.

Now picture a government program manager late at night, staring at a half-finished CPARS evaluation. The contractor delivered late, quality slipped, and costs ballooned. Everyone knows it. But hitting “submit” on an honest review would mean weeks of vendor rebuttals, legal letters, and paperwork. The easier path is obvious: mark “Satisfactory” or “Very Good,” close the file, and move on. That’s how truth gets buried in the federal past performance system.

CPARS is the government’s official tool for evaluating contractor performance, meant to record quality, timeliness, cost control, and management on every significant contract. In theory, it provides contracting officers and source selection officials with a trusted history of vendor performance. Instead of relying only on flashy proposals, the government could look at CPARS to see which firms delivered and which did not.

But in practice, the system doesn’t work. Ratings are inflated, negative reviews are rare, and narratives often say little. “Marginal” and “Unsatisfactory” ratings remain extremely rare; fewer than 2 percent of contractors receive those ratings. It isn’t because contractors never underperform (we read about failures everyday). It’s because government employees know that calling out poor performance leads to battles, not accountability.

The subjectivity of CPARS makes this problem worse. As one legal analysis put it, “performance evaluations are inherently subjective.” That subjectivity fuels disputes and discourages candor, since any negative comment can—and often does—become a fight. The message is clear: tell the truth and you suffer, play it safe and you coast. Over time, accountability gets punished and mediocrity gets rewarded.

Bad data on contractor performance fuels a self-perpetuating cycle of failure. As the Department of War Inspector General found, federal source selection officials often do not have access to timely, accurate, and complete past performance assessment information needed to make informed decisions about contract awards. Worse, unreliable data in CPARS can lead directly to contracts for poorly performing contractors. In other words, the very system designed to prevent mistakes may be causing them.

Why does this matter? Because past performance isn’t just a bureaucratic exercise. It’s supposed to be the government’s most powerful tool for accountability and value. In fact, past performance is one of the key pillars of evaluation when the government makes best value determinations, helping to ensure that contracts are awarded to those most likely to deliver successful results. Every vendor can promise lower costs, faster timelines, and innovative solutions. Past performance is the only evidence of whether they can actually deliver. If it worked, CPARS would be like a credit score: a trusted, comparable signal of reliability. Instead, it has become like a perverse version of Yelp, where every restaurant has five stars—useless for deciding where to eat.

The consequences go beyond paperwork. A ship delivered late isn’t just a number; it means a strike group isn’t where it needs to be. A poorly built IT system doesn’t just frustrate users; it leaves networks vulnerable to breach. A weapons program that doubles in cost doesn’t just waste money; it erodes readiness and forces trade-offs elsewhere. When CPARS fails to separate strong performers from weak ones, the government risks buying from the wrong companies. That means taxpayers pay more, warfighters get less, and missions suffer.

Imagine if sports worked this way. Instead of batting averages or quarterback ratings, every player was just labeled “Very Good.” Fans couldn’t tell the difference between a superstar and a benchwarmer. Teams couldn’t build rosters based on skill. That’s procurement today: a league with no scores, no standings, and no way to tell who’s any good.

There is a better way. Instead of relying on subjective narratives that invite disputes, the government could adopt a point-based meritocracy where performance is continuously measured across objective dimensions: timeliness, quality, cost discipline, collaboration, innovation, and value—measured not as the ability to meet requirements, but the ability to deliver more mission impact per dollar spent.

But to make this kind of system work, the government also has to rethink how it structures contracts. Most federal contracts today are compliance-based: they focus on whether a vendor delivered the required inputs or checked the right boxes. That makes it nearly impossible to measure value or reward innovation.

What’s needed is a shift toward outcome-based contracts, where success is tied to measurable results: ships delivered on time, systems that actually work in the field, or labor hours saved through automation. If the contract is framed around outcomes, then performance scoring has real teeth. A vendor that hits the outcome gets rewarded; a vendor that doesn’t sees it reflected immediately in its score. Outcome-based contracts align incentives, make continuous monitoring meaningful, and create the space for a real meritocracy to emerge.

But outcome-based contracts aren’t just a government fix—they require industry buy in, too. For decades, vendors have built their businesses around compliance: delivering the required inputs, producing the mandated reports, checking the boxes to prove effort. That model thrives in a CPARS world where “Satisfactory” is good enough and ratings rarely differentiate performance.

Shifting to outcome-based contracts demands more. It means companies must be willing to be judged on real results: systems that actually work in the field, ships that arrive on time, dollars saved, hours freed for government personnel. That’s uncomfortable for some vendors, because it raises the bar. But it’s also the foundation of trust. Industry should welcome this shift, because in a true meritocracy, firms that consistently deliver value will finally stand apart from those that just get by.

When both government and industry embrace outcome-based contracts, measurement becomes possible. Instead of fighting over narratives in CPARS, both sides could look at the same scorecard tied to outcomes. That transparency would create fairness for contractors, accountability for government, and confidence for taxpayers.

And crucially, this wouldn’t be a once-a-year report filed at contract closeout. It would be continuous monitoring, with performance scored and updated throughout the life of the contract. If a project slips behind schedule in month six, that dip would be reflected in real time. If a vendor course-corrects and recovers, its score would improve. That feedback loop would give both the government and the contractor visibility into how they’re doing, and the opportunity to fix problems before they spiral. It would also prevent the common CPARS trap where years of mediocre performance are hidden by a single rushed evaluation at the end.

Here’s how it could work:

• Timeliness: Contracts delivered on or ahead of schedule score higher. A program finished six months early might earn a 9/10, while one 12 months late gets a 0/10.

• Quality: Measured by defect rates or system reliability. A software system with 99.9 percent uptime earns a 10/10; one plagued by outages earns a 4/10 (or less).

• Cost discipline: Staying within budget is the baseline. Delivering savings earns additional points; repeated overruns lower the score.

• Collaboration and responsiveness: Assessed through surveys of government program teams—did the vendor problem-solve quickly, or did they drag out every dispute?

• Innovation and value: The hardest but most important. A shipbuilder that delivers on time, enabling fleet readiness, may score 10/10 even if costs were higher because the operational value outweighs the price. A contractor that automates manual processes, saving 20,000 government labor hours, earns a value score far above one that just meets the minimum spec.

These scores, updated continuously, would then be rolled into a composite—like a contractor “credit score” that moves with performance. Past performance would no longer be frozen in outdated paperwork. It would be alive, visible, and responsive to how contractors actually perform over time.

Such a system would change the incentive structure. Contractors couldn’t rely on relationships or intimidation to protect their records—they’d have to deliver. Government employees wouldn’t be vilified for telling the truth, because the truth would be built into the system itself. High-performing firms would finally have proof of their value. Poor performers would either improve or fall behind.

Some will argue that government contracts are too complex to be reduced to a score. But complexity is not an excuse for opacity. Airlines move billions of passengers while compiling metrics of safety, on-time arrivals, and customer satisfaction. Sports track performance across dozens of stats. Credit scores distill millions of data points into a number trusted by lenders. What matters is not precision, but differentiation—metrics that let buyers separate signal from noise.

The deeper issue is trust. CPARS fails not because people don’t care, but because no one trusts the data or believes it will make a difference. Contracting officers know the ratings are inflated. Program managers know that fighting vendors isn’t worth the effort. Vendors know that CPARS won’t make or break their next award. So the system lumbers on: ritualized, hollow, a performance evaluation system that performs nothing.

CPARS was supposed to bring meritocracy into federal contracting—a system where excellence rises, mediocrity falls, and results matter more than rhetoric. Instead, it has buried meritocracy under paperwork. By punishing candor and rewarding evasion, CPARS has created an anti-meritocracy where everyone looks the same on paper regardless of performance. And everyone gets a participation trophy.

A true meritocracy would look very different. Contractors that deliver readiness on time, create real mission value, and innovate and collaborate effectively would earn higher scores and more opportunities. Contractors that underperform, delay, or inflate costs would face real consequences. And government officials would no longer be stuck in a culture where honesty brings retaliation, because accountability would be hardwired into the system.

Fixing CPARS is about giving decision-makers the truth—and trust—they need to spend wisely and win decisively.

In a dangerous world, any Secretary of Defense should be able to get an immediate answer to a simple question: How much of each munition do we have right now—and where are they? This seemingly straightforward question has enormous implications for deterrence, resupply, and decision-making. Yet it triggers a cascade of emails, spreadsheets, and manual data pulls from dozens of disconnected systems, creating a strategic vulnerability that harms our ability to make time-sensitive decisions about force deployment, supply chain resilience, and resource allocation.

Now instead of artillery shells and missiles, think about dollars. The same problems that prevent us from tracking shells from the factory to the firing line plague the Department of Defense’s (DoD) financial statement audit: fragmented systems, disconnected processes, and zero real-time visibility into how money moves, what has been bought, and what cash is on hand. These problems have caused DoD to flunk seven audits in a row.

For decades, the DoD has attacked this problem with conventional solutions: setting aside billions for plans to consolidate financial and business platforms, developing data standardization frameworks, and hiring armies of consultants. While well-intentioned, this approach has delivered incremental progress at best. Already overworked staff scramble to fulfill requests with inadequate tools. Senior leaders get inadequate answers in return. Bad decisions get made on the basis of inadequate information. The cycle continues.

But what if that question didn’t trigger a chain of emails and spreadsheets? What if it triggered an AI agent? More precisely, an AI Auditor: a persistent, intelligent software agent built to continuously monitor, reconcile, and reason over every financial transaction and business event across the department. Unlike dashboards or static reports, the AI Auditor doesn’t wait for quarterly reviews. It lives inside the system. It understands how purchase requests become obligations, how invoices connect to disbursements and general ledger posting logic, and how financial data relates to operational reality as well as a financial statement line item. It flags anomalies before they become findings, drafts corrective actions, makes the fix before IGs issue memos, and learns over time from the best source of insight in the DoD: the people who do the work.

The DoD doesn’t need more dashboards. It needs decisions and action. It needs an AI audit operating system—one that can think like an auditor, scale like software, and work nonstop to get the DoD to pass a financial statement audit by 2028. To build that, the focus must shift away from adding more consultants and toward data integration and automation.

I helped build Advana with a small team in 2018 to unify the DoD’s fragmented business data. The initial goal was to ingest and standardize financial and business data to provide auditors with something usable—a “universe of transactions”—even if imperfect. Advana grew quickly, ingesting data across business systems, enabling commercial software, and becoming the backbone of financial and performance reporting. The initial results were promising. Lt. Gen. James Adams, Deputy Commandant for Programs and Resources, testified that the Marines’ unmatched and undistributed transactions dropped from $4.2 billion to just $200-$300 million because of Advana.

But while Advana integrated data and provided unprecedented visibility, it revealed a deeper truth: data aggregation alone cannot solve the audit. The problem extends beyond seeing the data—it requires understanding what it means in operational context and automatically taking corrective actions when problems arise. Aggregating data in one place improved transparency, but it didn’t encode the audit logic—the way transactions flow, the controls they pass through, or how audit findings should be automatically corrected. That requires reasoning, structure, and intelligent automation. Another key limitation of Advana was its inability to deploy tools that collected missing data directly from operators when no authoritative system existed—the kind of frontline inputs needed to resolve root-cause audit issues like unrecorded inventory, unmatched property transfers, or undocumented asset receipts. To truly solve the audit, Advana must evolve into an authoritative transactional system—not just aggregating existing data, but capturing it in an auditable, structured way as it’s generated by the operators.

Here’s the blunt reality: if the DoD wants to pass a financial statement audit by 2028, we don’t have time to retire legacy IT systems (the DoD has over 1,700 business IT systems). We don’t have time to reengineer processes. We should not hire more consultants or system integrators. And we definitely don’t have time to wait for the next monolithic system plan to fail. Each year, the audit adds new findings. Corrective actions stretch out. Audit teams rotate. Institutional knowledge disappears. The backlog grows (over 80% of the 2024 financial audit findings were audit issues found in previous years’ audits).

This isn’t just about financial compliance. Each finding is a fundamental impact to readiness, resource planning, and operational risk. Even successful audits of the Marines and DLA came at enormous cost. Thousands of Marines and civilians manually pieced together data that should have been integrated and automated from the start. That was heroic work, but heroism is not a scalable audit strategy. We need a paradigm shift to achieve a clean audit opinion at the department level, with complexities dwarfing those of the Marine Corps and DLA. Only then will we unlock the full operational value of a successful audit.

This is why Advana must now evolve into an AI audit operating system. The foundation is already there—the data, the platform, the users. The next step is encoding how auditors and managers think: the rules they follow, the exceptions they know, the risks they intuitively spot. That knowledge must become machine-readable and continuously applied to fix audit findings automatically. The kind of AI agent-based architecture needed to do that simply didn’t exist when Advana was born. Now it does.

Here’s how the AI Auditor works:

• Data Integration With Operational Context: The AI Auditor consumes every financial and business transaction across systems—from budget execution to procurement to payroll, with the plans and justifications behind all spending—leveraging the vast data already ingested and maintained within Advana. It connects every dollar spent to mission objectives, strategic plans, and operational requirements. This means it can flag not just accounting discrepancies but also misalignment between spending and strategic priorities.

• Ontology Creation: At the core of the AI Auditor is a dynamic framework known as an ontology—a living map of the audit’s key entities, relationships between financial elements, and the actions auditors and management take to verify them. The ontology captures both entities and relationships: funding authorizations flowing to contract line items, becoming obligations, generating invoices and receiving reports, triggering disbursements, and ultimately appearing in financial statements. Each connection creates a traceable path from appropriation to battlefield impact. What distinguishes this approach from traditional data models is its ability to encode expert actions. The ontology doesn't just represent what exists—it captures what should happen when discrepancies arise. It models the decision logic of experienced auditors: which exceptions matter, which patterns indicate risk, and which corrective actions resolve specific issues. By connecting transactions, business events, policies, people, and systems, it transforms fragmented data into meaningful, machine-readable intelligence. This enables AI agents to reason over financial information with auditor-level expertise, automatically resolving issues that previously required manual intervention. That's how static audit trails become dynamic audit intelligence.

• Automated Reconciliation: The AI continuously checks every line item and business event against the general ledger posting logic and business feeder systems, flagging mismatches and missing data or evidence based on rules and learned patterns.

• Anomaly Detection: Algorithms identify patterns, pinpoint potential fraud, pinpoint compliance issues, and surface high-risk transactions for immediate review.

• Corrective Action: Instead of waiting for quarterly reports, the AI Auditor issues real-time alerts and fixes issues automatically. The assertion of existence and completeness now becomes a metric that is tracked daily.

• Audit Trail: Every action, every transaction, every anomaly is logged and tracked, creating an indisputable audit trail based on a unified structure.

• Human-AI Teaming: The AI Auditor works alongside human auditors and management. Auditors and management review flagged transactions, validate or override recommendations, and provide feedback that continuously improves the system. Over time, the AI learns individual agency policies, common exceptions, and auditor preferences—not just from user interactions, but by reading and interpreting the hundreds of training documents, audit guides, policies, and app-specific PDFs scattered across the DoD. It captures and scales institutional knowledge that would otherwise remain siloed or disappear altogether.

• Data Collection When No Authoritative Source Exists: The AI Auditor doesn’t just work with existing data—it identifies where no authoritative data source exists and actively collects what’s missing. Whether it’s a receiving report, a property transfer, or asset condition data, the AI agent prompts end users at the point of action to enter what’s needed, creating structured, auditable records in real time. This is particularly vital for inventory and property, where the Army is already piloting efforts to digitize frontline data capture for items that were never properly recorded. The AI Auditor scales that effort by turning human-in-the-loop inputs into authoritative, audit-ready data—filling critical gaps that legacy systems were never designed to close.

• Multi-Agent Environment: The system operates as a network of specialized AI auditor agents, each assigned to specific financial and material weakness areas like payroll, contracts, asset management, and budget execution reconciliation. These agents work independently but share a common operating environment, cross-referencing findings and escalating potential risks to higher-level agents or human auditors/management.

Advana 2.0 (or should I say, AdvanAI) isn’t a data lake or a dashboard. It’s a full-fledged AI audit operating system—one that would transform the DoD from a reactive audit preparer into a proactive financial control engine. Built on a foundation of ontology and intelligent AI agents, this system addresses the root causes of persistent audit failures by identifying data quality issues at their source, pinpointing process breakdowns during system handoffs, highlighting unclear or contradictory policies, and adapting to rapidly evolving organizational structures. Unlike traditional approaches that simply flag discrepancies, AI Auditor incorporates rules, procedures, doctrine, and organizational context into its knowledge base, enabling it to explain anomalies and target likely issues for immediate resolution. This depth of understanding—far beyond typical financial review—transforms raw data into structured intelligence that AI agents can reason with and act upon, turning what would be educated guesses into expert-level financial oversight and automated corrective action.

The value of a clean financial statement audit opinion is decision advantage. It’s resourcing the warfighter faster. It’s visibility, accountability, and speed. The DoD doesn’t need more systems or consultants. It needs better thinking and action—at scales that can only be achieved by machines. When commanders have accurate, real-time information about resource availability, they can optimize deployment schedules, redirect assets to emerging priorities, and ensure forces never lack critical supplies in contested environments. Funds trapped in accounting errors become immediately available for modernization efforts and emerging capabilities, while improved visibility into assets and inventories strengthens readiness postures across all domains.

We won’t get there with spreadsheets. We won’t get there with dashboards. We won’t get there with more consultants. We'll get there with an AI Auditor working 24/7, learning, flagging risk, enforcing discipline, and automatically fixing issues before they impact mission execution. This system watches all, remembers all, and never lets a dollar go unaccounted for, making the audit continuous rather than episodic. It doesn't rip and replace legacy systems or ERPs—it rides above them, providing immediate value without disrupting operations. And it doesn't wait—it works now, enabling faster acquisition cycles, reducing administrative burden on operational units, and providing leaders with confidence in their resource decisions during competition, crisis, and conflict. That's what the warfighter deserves. That's what the taxpayer demands. And that's what the DoD can finally deliver—if we deploy it now.

What if every government analyst, program manager, or policy officer could be Tony Stark (Iron Man) and have their own AI co-worker like JARVIS? Not a sci-fi fantasy, but a real AI co-worker—an agent that could read memos, summarize policies, track spending anomalies, flag risks in real time, draft reports, offer decision-ready recommendations, and take action. Always on. Always learning. Always one step ahead. Not replacing the human mind, but extending it—scaling your intent, your oversight, and your ability to act. This is not a distant future. This is what AI agents make possible today.

Government isn’t short on data. It’s drowning in it. Every day, petabytes of information flow through agencies—contracts, emails, directives, budget lines, program metrics, policy drafts, FOIA requests, and more. Yet most of it goes untouched. It sits in SharePoints, PDFs, data lakes, and siloed systems, waiting for a human to find the needle in the haystack. Meanwhile, public servants burn out copying and pasting into Excel and PowerPoint. Analysts manually reconcile data sources that don’t talk to each other. Leaders are forced to make billion-dollar decisions based on outdated dashboards and delayed reports. The solution is not more dashboards. It’s more decisions—faster, more accurate, and more adaptive. That’s where AI agents come in.

AI agents are persistent, autonomous, and specialized digital teammates that can perceive, reason, and act on your behalf within a defined scope. They aren’t built to answer trivia—they’re built to watch your data, understand your intent, and take proactive steps to deliver outcomes. They don’t sleep. They don’t forget. AI agents replace bureaucracy.

Imagine an agent that monitors spending in real time, flags improper use of funds, drafts the corrective memo, and routes it—all before the auditor ever shows up. Or one that watches for duplicative contracts and proposes a consolidated buy. Or one that reads a new OMB memo and updates internal policy across systems and teams without anyone touching a spreadsheet. So next time you are considering hiring a human, maybe hold off until you’ve hired an AI agent.

To some this can feel like magic or impossible, but we don’t need to imagine if AI agents can transform operations—the proof is all around us:

• Insurance companies like AIG have automated underwriting with agents that analyze risk instantly and underwrite in real time.

• Tampa General Hospital reduced deaths from sepsis by using AI agents to monitor patient vitals and trigger interventions early.

• Engineering, procurement, and construction firms are automating their RFP responses—agents now write bid responses once done by entire teams.

• Eaton uses AI agents to automate price quoting for complex electrical equipment.

• Lear Corporation has deployed agents to review technical welding designs, improving speed and safety.

• Shopify has gone even further: AI use is now mandatory. If you want to hire for an open slot, you must first prove why AI can’t do it.

• Palantir is building AI FDEs—Forward Deployed Engineers—as agents. These digital counterparts scale the impact of real engineers, automate repeatable workflows, and make it easier to solve hard problems at scale.

These companies have demonstrated that you don’t need a complex, expensive, and time-consuming digital transformation. You need a digital teammate.

This might still feel like magic to some. Arthur C. Clark would tell the doubters that “any sufficiently advanced technology is indistinguishable from magic.” He’s right, and we’ve seen magical technological transformations before. When GPS first emerged, it seemed like science fiction: a small device triangulating your position by satellite, guiding you turn-by-turn. People doubted it. Now no one prints MapQuest directions. GPS didn’t just replace maps—it changed how we move, how we plan, and how we trust automation. AI agents are the GPS of government decision-making.

They process a landscape too vast for human cognition—budgets, memos, contracts, HR actions, regulations, operational data—and guide you toward insight and action. They improve with use. But just like GPS needed satellites and maps, AI agents need infrastructure. Without the right commercial software foundation, the magic fades. Here’s what it takes:

1. Ontologies: The secret sauce. Ontologies give agents a shared language of people, processes, policies, money, time, and mission. They make meaning machine-readable.

2. Tools: You need a platform that doesn’t just store data, but lets users build, deploy, and govern agents. Think version control, audit logs, and rules of the road for autonomy.

3. AI Models: Language models, retrieval-augmented generation (RAG), anomaly detection—these are the brains behind the agents. But they only work well when pointed at high-quality, contextualized/ontologized data.

4. Human Expertise: People still matter—deeply. You need subject matter experts to train, tune, and supervise agents. These humans become managers of intelligent workflows, not micromanagers of menial tasks.

This isn’t just a new technology wave. It’s a new operating model. Mark Zuckerberg has stated that in the future there will be more AI agents than people. Anthropic believes that fully virtual AI employees are a year away. Economist Tyler Cowen says AGI (Artificial General Intelligence) is already here. If you still believe this is magic, the way to manage that magic in government is to turn it into a contracting strategy. There’s a simple playbook: bake off the magic. Agencies can use Commercial Solutions Openings (CSOs) to select a back-office function—invoice validation, FOIA processing, audit, HR onboarding, you name it—and run a 90-day test with commercial vendors. Let the results speak. Use performance-based contracts tied to cost savings. Bake off the magic. Contract to those that deliver outcomes.

The reward? Government employees are freed from the toil of mandraulic bureaucratic processes and elevated into higher-level cognitive work. This isn’t about replacement—it’s about unleashing human potential.

Ironically, AI agents may be the best thing that ever happened to traditional system integrators and consultants—if they evolve. In the Age of the DOGE, time-and-materials and cost-plus contracts are dead. Success will belong to firms that stop selling labor hours and start delivering outcomes on commercial software.

Consultants and traditional system integrators will use their deep domain knowledge not to write reports or build static dashboards, but to train and maintain fleets of agents that learn, adapt, and perform on a backbone of commercial software. That model scales. That model wins. And it makes firm-fixed-price contracts not just possible, but desirable. Reduce cost to government, increase profit margin for industry. That’s the alignment we’ve always wanted—and AI agents can deliver it.

AI agents won’t make government perfect. But they’ll make it faster, smarter, and more adaptive. They’ll transform audits, acquisition, policy execution, and frontline services. They’ll replace toil with thought. Delay with speed. Complexity with clarity.

You don’t win by banning magic. You win by mastering it. Shopify already has. AIG already has. The world is not waiting. The only question now is: will government lead, follow, or fall behind? Because in the world of bureaucracy, the agency with the best agents—wins.

There’s a moment before every era ends — a final gathering, a familiar ritual, a sense that things will always stay the same. In some ways, legacy system integrators today are seated at their own Last Supper. The contracts still flow, the bodies still bill, and the slide decks still get delivered. But just outside the room, the world is changing fast — and the model that once fed entire empires is quietly breaking down.

I grew up going to Blockbuster. For my family, Friday night meant picking out a movie and ordering a pizza. It was a ritual — the blue carpet, the plastic cases, the smell of microwaved popcorn near the checkout line. It felt like magic. But that magic didn’t last.

Today, Blockbuster is a relic. One remaining store survives in Oregon — more a nostalgic Airbnb stop than a functioning business. And it didn’t have to end that way. In 2000, Blockbuster had the chance to buy a scrappy upstart called Netflix for $50 million. They passed. At the time, they laughed Netflix out of the room. Why would they bet on a mail-order DVD service when they had thousands of stores, millions of loyal customers, and a cash machine built on late fees? But that was the problem: they were too confident, too comfortable, and too convinced that what worked yesterday would work forever. Their arrogance blinded them. Their complacency kept them chained to a model already cracking beneath them. And in ignoring the future, they signed their own death warrant.

That story should feel familiar to government system integrators today. The industry is filled with giants — companies with deep benches, decades of contracts, and entire business models built on labor and length of projects. But the market is shifting underneath them. Slowly, then suddenly. And the opportunity to evolve — or be left behind — is very real.

The Department of Government Efficiency — DOGE — isn’t just a punchline. It’s a signal. It represents the growing demand across government for delivery models that are faster, cheaper, and outcome-driven. Agencies are no longer willing to wait five years for capability (if they get capability at all!). They’re done paying for effort instead of results. And they’re looking closely at every dollar spent, asking: did this move the mission?

System integrators built their empires in a world where complexity was profitable. They sold time. They staffed up. They responded to every new requirement with a new labor category. But that world is fading — fast.

Government leaders are now demanding productized solutions: commercial software, automated workflows, and repeatable outcomes. They want faster financial decision making, supply delivered to the right place, at the right time, and in the right amount, and improved readiness — not ten-year modernization plans. And they want it delivered like a service, not like a construction project. In short, they want their Netflix. Most of the industry is still running Blockbuster.

Blockbuster didn’t lose because people stopped watching movies. It lost because it tied its value to the wrong thing. It monetized store leases and late fees. It built a revenue model on friction. Netflix removed the friction entirely — delivering movies at home, then streaming them instantly, predicting what users wanted before they even searched, and finally created their own content. Blockbuster could have owned that model. But it was too comfortable. Too slow. Too invested in the way things had always been done even when it went against their customers needs.

Today, many integrators are walking the same path. They define success by number of FTEs deployed. They sell complexity. They win by building from scratch — even when commercial solutions exist. They still think the business is about the store. Not the experience. And many integrators are still feasting at the long table of legacy delivery — unaware, or unwilling to admit, that this may be their Last Supper.

And the numbers make the case impossible to ignore. Nearly 81% of federal IT projects are behind schedule and 45% are over budget. The average major defense program takes eleven years to deliver its first set of capabilities — if it finishes at all. Yet inside those programs, much of the work is repetitive — re-creating common capabilities readily found in commercial solutions such as data pipelines, AI integrations, and workflow frameworks, instead of focusing on their unique knowledge to solve customer problems.

The results of the traditional system integrators speak for themselves. Here is why the traditional system integrator model no longer works — and hasn’t for some time.

1. It’s too slow for today’s missions.

The traditional SI model runs on multi-year timelines, phased requirements, and waterfall delivery. Meanwhile, threats move at software speed. Government leaders can’t wait three to five years for capability — they need insights and actions in minutes, not decades. Speed is no longer a nice-to-have; it’s a strategic advantage. The old model simply can’t keep up.

2. It starts from scratch every time.

The classic SI playbook treats every project like it’s never been done before. New team, new codebase, new process. But across government, the same problems appear again and again — from logistics and finance to personnel readiness and data integration. Starting from zero isn’t innovation; it’s waste. Commercial software already solves 80% of these problems. Reuse isn’t a shortcut — it’s the new standard.

3. It monetizes effort instead of outcomes.

The SI business model rewards hours, not impact. Progress is measured by labor categories, not mission gains. But today’s government is asking different questions: How many decisions did this software accelerate? How many dollars were saved? How much risk was removed? If you’re not aligned to outcomes, you’re not in the conversation.

4. It avoids commercial software.

There’s an entrenched resistance to using proven tools — even when they already exist and work. Why? Because custom development keeps the labor model intact. But this only delays progress, increases risk, and drains resources. Leading with commercial software isn’t a shortcut — it’s a strategy. The future belongs to integrators who bring commercial platforms to the table, not paperwork.

5. It’s a magnet for the wrong kind of talent.

Top technologists want to build, not sit in meetings for five years. The legacy model attracts compliance managers over engineers, status trackers over problem solvers. As the demand for technical talent grows, SIs who can’t offer fast-moving, commercial software-led, mission-driven work will be left behind — both by customers and by their own workforce.

Those SIs that are willing to change, will win, and win big! Let’s be honest: this shift will mean fewer hours billed. Fewer slide decks. Fewer staff at the table. But it also means higher margins. Productized delivery and commercial software-first integration routinely deliver 2–3x margin improvements over traditional services work. Instead of chasing the next recompete, integrators can offer durable, reusable solutions with recurring value.

It also reduces risk. No more multi-year contracts that collapse under complexity. Productized delivery brings faster time-to-value, shorter delivery cycles, and stronger customer satisfaction. It builds trust — and scale. And it builds talent density. The best people want to work on problems that matter. They want impact. Not five-year roadmaps full of gate reviews.

Many system integrators will say, “We’re just delivering what the government asked for. They wanted level of effort. They wanted hourly billing.” But let’s be honest: it was often the system integrators — or the consultants advising them — who designed those models in the first place. The body-shop approach wasn’t an accident. It was a business strategy. And now that it’s no longer aligned with the mission, it’s time to own that truth — and evolve.

DOGE is just the beginning. The next generation of government leaders is digital-native, outcome-driven, and deeply skeptical of legacy models. They expect value on day one. They expect transparency. And they expect their partners to evolve.

For most, the room still feels warm. The pipeline is still full. But make no mistake — this is the Last Supper of the system integrator as we’ve known it. The chairs are still filled, but the menu has changed. The ones who stay too long will be left with crumbs. The ones who leave early to build something better will own the future.

The system integrators that have the courage to break from the old model — to stop billing by the hour, to stop solving the same problems from scratch, to lead with commercial software and deliver real outcomes — won’t just survive this shift. They’ll win. And they’ll win big. They’ll build leaner, faster, higher-margin businesses. They’ll attract the best talent. They’ll earn trust by delivering impact — not burn rates. And they’ll be the ones shaping the next era of government delivery, not clinging to the last one.

For those willing to leave the table before the Last Supper is over, there’s still time to cook something entirely new — and far more valuable.

For eight years, the Department of Defense (DoD) has attempted to pass a financial audit—and each year, it has failed. The cost? Nearly $10 billion on audit efforts (that is almost all human labor!), with $1.4 billion in 2024 alone. That’s enough to employ 6,000 people for a year—or, more meaningfully, to fully man one aircraft carrier, an entire Army brigade combat team, or 80 Air Force fighter squadrons.

And yet, despite the scale of the investment, the audit never seems to get any closer to completion. The same issues persist year after year. Over two-thousand audit findings were reissued from previous audits in 2024 alone—that’s thousands of transactions that can’t be reconciled, items that can’t be found, and systems that can’t talk to each other. To put this in perspective, over 80% of the 2024 financial audit findings were audit issues found in previous years’ audits. The DoD isn’t just failing a financial audit. It’s failing to see the financial audit for what it should be: a tool for warfighting readiness, not just a compliance exercise.

The reality is that financial compliance alone is not enough—true success is measured by how well financial, logistics, and other business systems support warfighters in real time. Yet DoD’s approach to audit remediation is designed to sustain inefficiency rather than solve it. Consider the following:

• The DoD operates on 1,700+ fragmented and outdated business systems—many redundant, few ever retired.

• MOCAS, the world’s oldest operational IT system, has been tracking DoD contract payments since 1958, back when the U.S. had just launched its first satellite. In addition, critical DoD financial management systems like SPS, GAFS, OnePay, CAPS, STANFINS, SOMARDS, and IAPS are over 40 years old, on average!

• Audit support is structured as a labor-based industry, driven by cost-plus and time-and-materials contracts that pay for effort, not results. The longer audit remediation takes, the more money firms make. This labor-based model causes problems even when a DoD organization passes a financial audit. For example, the United States Marine Corps (USMC) did pass an audit, a major milestone for the DoD! But this success was achieved on the backs of heroes—thousands of Marines and civilians putting in enormous amounts of manual labor and finding creative workarounds to piece together financial data that should have been automated from the start.

Audit is one of the most transformative initiatives to disrupt the Pentagon in decades, yet it remains outsourced and sidelined in favor of outdated business practices. The reluctance to change the process reflects a broader resistance to modernizing the enterprise, leaving business systems stagnant.

A financial audit isn’t just about balancing the books. When executed effectively, it becomes a force multiplier—driving warfighting advantage, financial transparency, and cost efficiency:

• For warfighters: Property accountability must be directly tied to operational effectiveness and readiness. Knowing where equipment is from factory to foxhole, what condition it is in, and how quickly it can be deployed is critical to warfighting success. The inability to accurately track assets doesn’t just impact audit opinions—it affects combat effectiveness and sustainment in real-world operations.

• For financial transparency: A modernized audit process should close the cash chain, ensuring that funds flow efficiently from appropriation to execution. Right now, financial opacity in the DoD and Congress leads to delays, lost purchasing power, and funds trapped in inefficient bureaucratic cycles. With a software and data-driven financial system, the DoD and Congress can track dollars—ensuring resources are allocated to the mission at speed.

• For reducing audit costs: The Independent Public Accountants (IPAs) responsible for auditing the DoD are forced to manually recreate financial records, performing exhaustive reconciliations to prove existence and completeness. By automating these processes—including reconciliations, inventory validation, and internal controls—the DoD can reduce the need for thousands of auditors and shorten the time required to validate financial statements. Systems that automatically demonstrate compliance will drastically reduce external audit costs and improve confidence in financial reporting.

Instead, the DoD has built an audit industry that feeds itself, with billions spent on manual processes, contractors reviewing transactions line-by-line, audit coaches, and stale dashboards that inform but don’t act. Technology exists to fix this, but it is not being used at scale.

Fixing this problem does not require more people (it requires fewer!). It requires software, ontologies, analytics, intelligent document processing, AI, and agentic automation that can be integrated to extract meaningful outcomes across the Department’s many data silos. Software-as-a-Service (SaaS) is already transforming all business functions in the private sector, using AI, machine learning, and ontologies to automate routine tasks—allowing business professionals to focus on strategic decision-making rather than manually counting things. The DoD must move away from labor-driven compliance work and toward a software-driven business system that ensures accountability and efficiency in real time.

Elon Musk understands that inefficiency is a national security risk. President Trump’s Department of Government Efficiency (DOGE) aims to cut outdated processes that slow progress. If Musk had $1.4 billion, he wouldn’t waste it on manually reconciling spreadsheets, tracking paper trails, and hiring consultants. He’d automate, optimize, and deploy AI (including Agentic AI) to solve the problem at speed and scale.

This isn’t science fiction—it is how modern financial and logistics systems operate in the private sector today. The DoD just hasn’t embraced it. A commercial software-driven audit system wouldn’t just validate financial transactions—it would directly impact operational outcomes. A financially optimized force is a more lethal force!

To achieve compliance and unlock warfighting value, the DoD must take the following steps:

1. A New Approach to Business System Modernization

The DoD must move away from fragmented, legacy business systems that were never designed to operate at the speed and complexity of today’s defense environment. A different approach is needed—one that doesn’t just replace old systems with more custom-built or monolithic architectures, but instead integrates data across financial, logistics, and operational domains in real time.

Modern commercial data integration, analytics, transactional, and AI platforms provide an alternative to the traditional model of building separate systems for every function. Instead of a new wave of costly systems that will become obsolete in a few years, the DoD should adopt a scalable ontology-based open architecture that connects existing data sources, automates workflows, and eliminates redundant systems altogether.

By using a modern ontology-based operating system for defense business data, the DoD can:

• Eliminate outdated and duplicative business systems instead of perpetually upgrading broken infrastructure.

• Automate reconciliation, financial tracking, and asset visibility across business and warfighting domains.

• Ensure auditability and financial transparency as a built-in function, not a separate compliance burden.

This is not just a shift in technology—it’s a shift in how the DoD thinks about business systems. Instead of continuing the cycle of replacing old software with new isolated solutions, a fully integrated, open, data-driven ontology-based approach will ensure real-time decision-making, automated cross-functional workflows, financial accountability, and warfighting readiness.

2. Expand and Evolve Advana Beyond Dashboards

Advana has been underutilized for the last 18 months, and its integration approach remains overly reliant on manual, labor-intensive integration processes (Greg L knows—he founded it!). To reach its full potential, it must evolve into the DoD’s central system for financial and business operational analytics, workflows, and automation.

This requires continued backing from senior DoD leadership and an aggressive push to expand the universe of auditable data, mapped to a common ontology that ensures financial and business operational transparency. More importantly, it demands a shift from passive dashboards to real-time financial tracking, automated workflows, and AI-driven reconciliation. By aggressively integrating commercial software and AI-powered automation, Advana can move beyond visualization and become a true engine for audit success and business decision-making at speed.

3. Adopt Firm Fixed-Price Contracts. Change Audit Remediation Incentives

The DoD must stop paying for inefficiency. Instead of rewarding labor via large consulting contracts, it should pay for audit success and business integration. Firm fixed-price contracts ensure contractors are incentivized to automate and solve problems, not prolong them. Outcomes and speed matter!

4. Deploy Commercial Software for Audit Integration and Remediation

The DoD has relied too long on custom-built government software and large consulting firms that lag behind industry standards.

• Software firms already deliver AI-powered business solutions at scale in logistics and audit automation.

• They move faster than traditional government consultants.

• The DoD must shift from a workforce of neurons (human labor) to a system of electrons (software)—replacing manual audit efforts with software-driven, automated, and AI-enhanced financial oversight. There are a few enlightened system integrators that are starting to get this balance right.

By making these changes, the DoD can finally move beyond compliance and use the audit process as a tool for operational readiness, financial speed, and strategic advantage.

While the Pentagon struggles to audit itself, China is rapidly modernizing its systems with AI and automation. Their military-civil fusion strategy ensures financial and operational decisions are fully integrated—giving them an edge in speed, transparency, and adaptability. This is not just an inefficiency problem—it is a national security problem.

Yet, modernization is not just a technology challenge—it’s a cultural one. The DoD rewards the status quo and sidelines those who push for change. Every outdated business process and aging system has its protectors—people who resist transformation out of fear, optics, or the comfort of the old system. Until this mindset shifts, innovation will remain an uphill battle, and inefficiencies will continue to drain resources from warfighters. The Pentagon cannot afford to let bureaucracy stand in the way of operational superiority.

The good news? The Navy and Marine Corps are already moving in this direction by implementing the recommendations above. Both Services are actively working to integrate software-driven financial management solutions that align with operational needs, closing the cash chain while improving warfighting readiness.

The Navy and Marine Corps understand what Sun Tzu wrote centuries ago: “If you know the enemy and know yourself, you need not fear the result of a hundred battles.” Audit and financial transparency are not just compliance goals—they are the foundation of understanding our own capabilities, ensuring resources flow efficiently, and maintaining operational advantage in every fight.

The time for incremental progress is over. The future belongs to those who move fast, automate ruthlessly, and act boldly.

Tony Stark, the legendary genius in the Iron Man suit, was not a born superhero. Neither does he gain powers through exotic serums or electrical storms. He is extremely intelligent, but so are many men and women. Tony’s superpower is uniquely inherent in his remarkable vision to blend human ingenuity and cutting-edge technology. And contrary to the moniker Iron Man, the secret to his success is not the metal flying suit—it is the operating system companion coaching him through every battle with superhuman computations, simulations, data analytics, and strategic support. His super creation J.A.R.V.I.S. is the AI that brought life and intelligence to the suit. Without J.A.R.V.I.S., Tony Stark is just a man encased in metal.

Tony Stark didn’t stop at J.A.R.V.I.S. He saw the potential of AI not just to empower individuals or individual weapon systems, but to manage entire organizations like Stark Industries and the Avengers. Enter F.R.I.D.A.Y., the advanced AI assistant created to handle the sprawling complexity of superhero conglomerate global operations. While J.A.R.V.I.S. was the trusted ally that elevated Stark’s heroics, F.R.I.D.A.Y. was designed to also oversee and coordinate large-scale activities, serving as the operational backbone of the enterprise. This AI assistant embodied a new strategic paradigm: the upgrade from supporting a single operator to orchestrating multifaceted operations. F.R.I.D.A.Y. was more than a tool; it was the central hub for the team, enabling swift and efficient decision-making that kept Stark Industries and the Avengers winning.

For the Department of Defense (DoD), the lessons from Stark’s approach are profound. The future of national security depends on harnessing a dual-tier AI system: a J.A.R.V.I.S. that empowers the individual warfighter and weapon system and a F.R.I.D.A.Y. that manages and optimizes operations at scale. The challenge lies in building an AI and data architecture for AI and human teaming that can integrate these capabilities effectively and securely.

Pre-Built AI Products
Tony Stark would start with pre-built AI workflows and products that are available to the entire organization. In simple terms, workflows are sequences of automated processes that handle tasks or data, making operations more efficient and effective. For J.A.R.V.I.S., this would include access to a suite of tactical tools that provide real-time battlefield analysis, enhance situational awareness, and suggest actions to the warfighter. J.A.R.V.I.S. would become a soldier’s highly synced battle buddy and rear support; instantaneously fusing exquisite intelligence with real-time tactical inputs, closing the OODA loop in milliseconds. For F.R.I.D.A.Y., these pre-built AI workflows would be accessed to manage complex large-scale military operations, integrating logistics, troop movements, and intelligence data to provide an overarching view. F.R.I.D.A.Y. would act as the nerve center for operations, coordinating across units and enabling commanders to make informed decisions rapidly.

Custom AI Products
Custom AI products would be the next step. These AI solutions are specifically tailored for unique mission challenges. Operators would develop mission-specific algorithms and workflows that adapt in real-time, anticipating enemy tactics, calculating tactical maneuvers, and guiding the warfighter toward safer and more effective outcomes. F.R.I.D.A.Y., along with her team, would create custom AI-enabled workflows to run complex simulations, predict logistical requirements, and model strategic scenarios, ensuring that military leaders are equipped with the tools needed to address any operational challenge. This blend of pre-built workflows and the capability to design custom AI workflows provide organizations with the flexibility to meet diverse mission objectives. It allows them to leverage existing solutions while tailoring them to specific mission requirements as needed, ensuring readiness and adaptability across any situation.

The Ontology Layer
The Ontology Layer, representing the nouns and verbs or digital twin of an organization, is where the real magic happens. In simple terms, an ontology is a structured framework that helps an AI system understand and connect pieces of information in a common and meaningful way, turning raw data into actionable insights. For J.A.R.V.I.S., this would mean integrating data from sensors, communications, and environmental inputs into a cohesive picture of the battlefield leveraging pre-built, software-defined data connectors. This capability would allow J.A.R.V.I.S. to provide proactive, context-rich recommendations, helping the warfighter navigate complex situations with clarity and precision. For F.R.I.D.A.Y., the Ontology Layer would map data across entire operational landscapes, connecting logistics centers, finance, procurement, readiness, intelligence feeds, and command stations into a unified, interactive framework in the form of an open Ontology Software Development Kit (O-SDK). The O-SDK (i.e. open APIs) would empower decision-makers with the ability to visualize and act on complex, interconnected data seamlessly and securely across multiple platforms, making F.R.I.D.A.Y. an essential partner in strategic planning and execution.

Data Services
Data services would serve as the vital circulatory system of these AI systems, encompassing the robust infrastructure needed to move, process, and store data in a way that ensures rapid access and usability by AI. Beyond simple data handling, these services would include advanced data lineage tracking, granular access controls, and pre-built data quality checks to foster operator trust by confirming the accuracy and reliability of information. J.A.R.V.I.S. would leverage these enhanced data services to seamlessly tap into real-time streams from drone footage, satellite surveillance, and troop communications, parsing and relaying critical insights back to the warfighter with zero latency. F.R.I.D.A.Y. would elevate this even further, synthesizing vast amounts of intelligence, logistics data, and situational reports to provide commanders with a complete, real-time operational picture. In addition, F.R.I.D.A.Y would write back actions to the appropriate systems to ensure each process step is as automated as possible. This seamless data integration between operators and machines would empower both J.A.R.V.I.S. and F.R.I.D.A.Y. to function at peak efficiency, shifting from merely reactive responses to strategic, proactive decision-making that stays one step ahead of the mission's demands.

AI Services
AI services would enable continuous learning and advanced capabilities. These services refer to the computational tools that allow an AI to learn from experience, run complex models, and adapt to new information. Central to this approach is the integration of generative AI, which enhances human-AI teaming by enabling more dynamic interactions and richer support. The use of a multi-LLM (Large Language Model) strategy allows J.A.R.V.I.S. and F.R.I.D.A.Y. to harness the strengths of multiple AI models, drawing on their diverse capabilities to provide more nuanced and responsive assistance.

What makes this truly effective is the Ontology Layer. The previously mentioned ontology acts as the structured framework that enables J.A.R.V.I.S. and F.R.I.D.A.Y. to understand the organization, its data, and its operations comprehensively. This understanding allows generative AI to function seamlessly, translating complex information into actionable suggestions that humans can easily understand and interact with. It bridges the gap between human operators and AI, facilitating real-time collaboration and informed decision-making.

J.A.R.V.I.S. would learn from each mission, refining its support and improving its responses based on past outcomes, making it smarter and more attuned to the warfighter’s needs. Generative AI, empowered by the ontology, would elevate this process by allowing J.A.R.V.I.S. to generate real-time, adaptive solutions in a format that enhances human comprehension and interaction. F.R.I.D.A.Y. would leverage AI services to run large-scale simulations, optimize resource allocation, and anticipate risks, ensuring that operations remain efficient and adaptable. This dynamic model usage, underpinned by generative AI and the ontology, would make F.R.I.D.A.Y. the strategic engine capable of turning complex plans into executable missions, offering high-level insights and recommendations tailored to real-time demands.

Workflow Services
Workflow services would bridge the gap between human operators and these advanced AIs. Workflow services facilitate the automation and management of tasks, making collaboration between humans and AI smoother. For J.A.R.V.I.S., this would mean guiding the warfighter through mission steps with clear, actionable prompts and decision-support tools without overwhelming them. For F.R.I.D.A.Y., workflow services would coordinate across various military branches, ensuring operations are synchronized and aligned. This would allow both AIs to act not just as assistants but as integral mission partners, enhancing human decision-making at every level from the boardroom to the battlefield.

Security and Governance
Security and governance would be built into the core of these systems. Security and governance encompass the protocols and policies that protect data and ensure compliance with regulations. J.A.R.V.I.S. would operate with stringent measures to safeguard mission-critical data and maintain integrity, preventing any tampering or security breaches. F.R.I.D.A.Y. would extend these protocols to cover large-scale operations, securing communication channels and protecting strategic plans from potential threats. This robust security framework and granular access controls would create a resilient, trusted environment for mission-critical activities.

Software Delivery
Finally, the software delivery layer would ensure that J.A.R.V.I.S. and F.R.I.D.A.Y. stay at the cutting edge of capability. This layer refers to the infrastructure that supports the deployment and updating of software without interruptions. Just as Stark constantly tinkered with his suits to adapt to new challenges, these AI workflows would receive regular updates, new functionalities, and iterative enhancements across any classification network. This would keep them agile and capable of incorporating new tech and methods, ensuring they remain highly effective in any situation.

Iron Men and Women
Tony Stark’s vision was never just about creating machines—it was about building AI partners that amplify human capability and individual weapon systems to new heights. J.A.R.V.I.S. would be the warfighter’s digital ally, providing real-time intelligence and support in the heat of battle. F.R.I.D.A.Y. would be the strategic conductor, managing large-scale operations and ensuring decisions are made with speed and precision. For the DoD, adopting a similar dual-tier approach means transforming leaders into modern-day Iron Men and Women, equipped not just with strength but with intelligence, adaptability, and strategic foresight. The future of defense isn’t just about power—it’s about being smarter, faster, and more resilient with AI as an indispensable ally.

As any student learns in an introductory economics class, there is no such thing as a free lunch. And yet, many businesses contribute extensively to open source libraries, choose to open source entire product lines, and even pay other companies to improve their open source offerings. The U.S. government, for its part, often encourages vendors to use only free and open source software (FOSS) components in their software offerings (versus paid commercial software). So where is the hidden cost?

To understand why FOSS isn’t free, we first need to understand the incentive structure motivating businesses to pursue open source. A helpful corollary is the concept of complementary goods, such as gas and cars. When the price of gas goes down, the demand for cars goes up, which subsequently increases the price of cars. This means businesses are motivated to find a way to make complementary products cheaper.

Put another way, businesses are incentivized to commoditize their complement.

Joel Sapolsky makes this point is his classic 2002 post on open source: “I noticed something interesting about open source software, which is this: most of the companies spending big money to develop open source software are doing it because it’s a good business strategy for them, not because they suddenly stopped believing in capitalism.”

He goes on to give the example of IBM spending millions to develop open source software not out of altruistic motives but because IBM was becoming an IT consulting company. “IT consulting is a complement of enterprise software. Thus IBM needs to commoditize enterprise software, and the best way to do this is by supporting open source.”

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Palantir has more than 200 open source projects. The investment of engineering hours into these projects makes them expensive. Palantir pursues this strategy because we’re either commoditizing a complement that enables more budget to be available for our category of software, or we’re reducing friction to a sale by delivering anti-lockin mechanisms. For example, in 2012 Palantir paid for PostGIS to build open source geospatial features because we wanted the budget customers would pay for databases backing Palantir's software to instead be available for our software.

In today’s Department of Defense context, open source is maximally aligned with companies that bill hours and put butts-in-seats rather than sell software. Open source-only solutions enable traditional, labor-based Systems Integrators to commoditize their complement — commercial software — and retain maximum share of government budget for billable hours.

To be clear, there is nothing nefarious about this strategy. In a balanced market, this is a powerful way to drive competition and price-performance of commercial software companies. It rightfully puts the onus on Palantir and other companies charging for software products to prove outsized value over freely available alternatives. This spurs innovation.

The problem arises when the Government kills the market mechanism by prohibiting the use of commercial software in its solutions. When the Government hands Systems Integrators the budget for billable hours rather than making them compete with commercial alternatives, it rejects the American belief in the value of competition and markets. USGIF’s recent NRO Industry Advising Working Group report, “Reducing Barriers to Uptake of Commercial Technology,” to which Palantir contributed, identifies several recent government solicitations discouraging the use of commercial software:

• “We are seeking innovative approaches that include…reduced dependencies on licensed software products through the use of free and open-source (FOSS) software.” “Attributes for [the desired] architecture” include “maximizing use of FOSS.”

• “The Contractor shall design and integrate to preclude long-term dependence on closed/ vendor unique or proprietary interface standards, technologies, products or architectures.”

• “The contractor must obtain NRO permission before delivering software under this contract which incorporates any other software that is not free and open source.”

The relentless focus for any product or system involving software should be the end-to-end delivery of a solution to drive an outcome. Outcomes should take precedence over requirements that serve as poor proxies for what we hope will translate to outcomes. The success of cloud computing as-a-service over open source alternatives (e.g, OpenStack) demonstrates how incentives affect the pace of innovation. Open source innovates at the pace of the party commoditizing something (at the limit). Commercial software as-a-service innovates at the pace of winning the marginal customer; it forces the software developer to feel the pain of cloud optimization, performance, DevOps, and feature innovation.

Users want a service, not a file with code in it. Open source competition for Windows did not work. No one was going to win the consumer or enterprise market with Linux. There were niches of developers using different versions of Unix, but there was no obsession with creating a product. Ultimately, Apple did win the market by building an Operating System on top of OpenSource BDS to make MacOS. It commoditized its complement to enable custom services and products (e.g., App Store, Apple TV, iCloud, and the hardware required to leverage those services).

For a humorous example, here is an infamous Hacker News commenter who argued in 2007 that Dropbox as a product didn’t make sense because anyone could “build such a system yourself quite trivially by getting an FTP account, mounting it locally with curlftpfs, and then using SVN or CVS on the mounted filesystem.” This of course ignores the millions of users who wanted the convenience and white-glove experience of having a fully managed file storage system whose security and updates they didn’t need to worry about.

In its quest to commoditize its complement, Systems Integrators have succeeded in creating a narrative that only FOSS liberates the Government from vendor-lock. But the only way to build a mission system or product from FOSS components is by implementing custom code. Per the USGIF report, “ironically, the complexity and bespoke nature of FOSS-based systems drives outcomes the Government is trying to avoid: increased technical debt and vendor lock to the developer.” For example, one government agency aggressively avoided a popular commercial SIEM solution only to find it was locked into the custom alternative it built.

The Government’s preference for exclusively FOSS solutions does not save money. It just shifts the expense from commercial software to labor. Zooming out, this hurts American prosperity, since $1 spent by the Government on labor is worth ~$1-2 of wealth in American retirement accounts. That same $1 is worth $5-20 when spent on software because the market values the revenue of software companies at 5-20x the revenue of the traditional primes. The increased market value generates more taxes through capital gains, which funds the very Defense enterprise.

American prosperity - growing the economy - underpins our nation’s security.

Power Law vs Normal Distribution

The warfighter lives in a power law world, but Agile is optimized for normal distribution.

Many variables follow a normal distribution, like height, athletic ability, and software engineering talent. Agile incorrectly treats user needs like a normally distributed variable: feature requests and requirements enter a prioritization queue that biases to the average need. By managing to the mean, Agile (or any centralized management system) is designed to protect downside and avoid failure.

While some things are appropriately prioritized centrally, like a large backend effort, the most creative efforts can’t be managed this way without destroying all the value. A power law distribution roughly follows the 80/20 rule, where 80% of the outcomes result from 20% of the causes. A limited number of user needs and features will drive the majority of results.

Discerning the correct place to invest is hard and often not knowable ex-ante. It depends both on the distribution of creative commanders who are ready to wield software as a weapon system and on the real-world events catalyzing invention and innovation. The ideas and concepts from, say, a combatant command that define the next paradigm when translated to software would only be weaker if averaged against the requests from all the combatant commands.

In the case of JADC2, imagine a never-ending queue of minor improvements and adjustments that are centrally ranked and scheduled for implementation. At the same time, there are users with varying levels of connection to modern warfare who have immediate and diverse use cases and needs. By relying on a centralized prioritization system like Agile, software development will focus on the most substantial changes that cater to a homogeneous set of collective user needs, instead of addressing the most urgent, specific requirements for current day operations. Moreover, priorities might change as new commanders join the field and contribute their insights.

In adopting a dynamic, decentralized approach to software development, the warfighter is better served by enabling rapid engineering and integration of daily alterations, which would otherwise be lost in the bureaucratic process of change management cycles, no matter how agile. This way, innovation is de-bottlenecked, and the system is more responsive to the specific and evolving needs of users in the field, ensuring the most compelling concepts can scale across the enterprise regardless of how they emerge.

Attempts by the military-industrial complex to transubstantiate the Primes into software-first companies have not succeeded. The legacy Systems Integrators don’t know how to sell software to the government, the government barely knows how to buy it, and Agile and DevSecOps have been poorly applied to old processes. The primacy of the Software Integrator is evident, and the commercial world provides a cautionary tale for what happens when there is a reluctance to embrace this role. Legacy conglomerates and manufacturers have hired hundreds of thousands of software engineers and committed hundreds of millions of lines of code with little to show for it. Take General Electric's digital transformation effort with Predix, or Toyota’s troubled Woven Planet software city. HSBC once proudly said they had more software developers than Oracle. Ford built a huge software engineering department. VW started CARIAD to build automotive software that would rival Tesla, but has been delayed in realizing results as they’ve worked to assemble the right team to execute on their mission.

Defining Systems Integrators vs Software Integrators

The traditional Systems Integrator optimizes for exact management of schedule, performance and cost with well-defined interfaces and processes. The Software Integrator optimizes for eliminating bottlenecks and ensuring the pebble is in the right shoe. The first line of defense for the Systems Integrator is people and processes. The first line of defense for the Software Integrator is - you guessed it - software, delivered via an organizational and technical architecture that scales to ensure every supplier (both hardware and software) moves at the speed of the mission.

The Software Integrator has two objectives:

1. Enable a diverse and competitive ecosystem of suppliers to compete and win at the speed of necessity, not requirements. The barrier to entry with software is lower than it is for systems, so new vendors can quickly be brought into a program to prove their mettle. Compare this with the multi-month (often multi-year) process to onboard new vendors to a program with a Systems Integrator. Software provides continuous competition, continuous integration, and continuous delivery.

2. Enable systematic visibility into performance for the government through software. Government Purpose Rights (GPR) interfaces and well-defined software primitives ensure MOSA compliance and application level interoperability by default. The most crucial aspect for all this to work is that each vendor must be Operationally Responsible.

And it does so by enabling the underlying software providers in the program to be Operationally Responsible (OR). Under this OR framework, the group that builds the software is responsible for operating the software and supporting it as-a-service.

This incentive alignment is critical to serving the warfighter. When software doesn’t work, the OR developer gets paged, morning or night, weekend or weekday. The pebble is in the right shoe. In return for being responsive to bugs and suboptimal features, vendors are rewarded with maximum surface area to innovate in how they develop, manage, and deploy their software. Rather than wait for requirements from the DoD dictating how a problem must be solved, vendors send their team to the frontlines to incorporate warfighter feedback in real time. OR not-so-subtly shifts the focus from requirements to outcomes.

To illustrate, let’s walk through a notional example of how this works. A forward deployed engineer from one of the many vendors on a Software Integrator program is iterating with users at a Combined Air Operations Center (CAOC). The vendor has built a target-effector pairing application and is operationally responsible for its deployment at the CAOC. Users want the ability to create tactical support requests for engagements against targets (e.g., asking for additional assets, munitions, or effects). To implement this new feature, the engineer heads to their rental car in the CAOC parking lot, opens up her laptop, and works for an afternoon.

The typical Systems Integrator would take months to implement this feature. With a Software Integrator providing enabling infrastructure, the vendor with the target-effector pairing application will be able to deploy the newly coded feature to users within 24 hours.

• The Software Integrator provides automated testing and validation to ensure continuous integration during development. For example, every code commit must be signed with a valid GPG token assigned to the developer, ensuring the code was authored directly by the signer of the commit.

• Prior to deployment low-side, there is a tiered approach to reinforcing checks that ensures software is secure and complies with the pre-defined software supply chain specifications. There are automated deployment, update, and recall procedures in the Software Integrator’s continuous deployment pipeline. To avoid downtime, upgrades are rolled out incrementally. Any recalls through this process will immediately redirect traffic to the non-recalled versions, ensuring upgrade safety and continued quality of service.

• A version of this process repeats when it is time to deploy the feature high-side. Service binaries are packaged on a 4-hour cadence and transferred to SIPR and JWICS after the service version upgraded healthy on the “stable” channel. Throughout this process, the Software Integrator’s infrastructure continuously monitors performance, stability, and security via real-time telemetry mechanics to advance the authored change to the warfighter in an automated fashion.

Later that same day, the software vendor rolls out the new feature to the CAOC. During an exercise, a user requests an advanced medium-range air-to-air missile (AMRAAM) to replenish depleted supplies and demonstrate a targeting of a notional electronic warfare site.

Implications for Hardware

Metal must still be bent. Production matters. Using up ten years of munition production in ten weeks in Ukraine underscores that point. But increasingly, production is itself software-defined and optimized. Despite earlier examples, there are plenty of commercial enterprises that have successfully leveraged software to improve production without undertaking in-house digital transformations:

• Panasonic Energy of North America (PENA) makes its batteries in a jointly operated Tesla gigafactory.

• When the A350 production was struggling to ramp, CEO Tom Enders turned to Palantir and our software to reduce defects, improve quality, increase yield, and improve subcomponent assembly performance — 1000 lead bullets fired through a software barrel by a Software Integrator.

• bp optimizes hydrocarbon production with software, yielding $3bn/year of incremental production.

• Eaton uses AI for a 25% increase in productivity and the ability to dynamically prioritize material shortages.

• And Rio Tinto uses software to increase production efficiency in its mining operations.

The value of software extends to the Defense Industrial Base (DIB), where two primes use Palantir to optimize their own production. But this is the exception, not the rule. Government incentives play a big part. A U.S. shipbuilding company left improved productivity on the cutting room floor because it would adversely affect their revenue and profits in future recompetes for the contract, which they were confident they would win. Because of government-regulated margins, the shipbuilding company would be penalized for increased efficiency. This is exactly the wrong math to be doing when China’s shipbuilding capability is reportedly 200x greater than our own.

Nobody believes software companies can flip a switch and become hardware companies (Palantir doesn’t build radios for TITAN; we rely on world-class technology from L3H). Many industrial software companies sold the vision but fell short of becoming software companies. We still need our Primes, but we also need new Software Integrators.

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Conway’s Law can be understood through this lighthearted and extreme caricature of some of America’s largest tech companies. Even as a caricature, it roughly explains why different companies succeeded or failed in the face of different disruptions and opportunities. Microsoft in the early aughts was too busy fighting internally to seize the opportunity presented by mobile (ceded to Apple and Google) or cloud (ceded initially to AWS). Google is so matrixed that it moves slow and couldn’t seize on Cloud even though they invented Kubernetes, map reduce, and basically all of the foundational technologies of the modern cloud. Apple is functionally a series of Special Access Programs tightly managed to create exquisite, discrete offerings. Meta is a flat web of peer to peer experimentation and innovation. And so on.

Conway noted that when various organizational divisions or groups within a bigger organization collaborate on a more extensive system, they would inevitably divide the system into components that each team could work on autonomously. Subsequently, they would determine how these individual systems would interact with each other via a specific communication method, resulting in an end product that resembled the organization.

Funding services to drive progress via Army’s Convergence, Navy’s Overmatch, and Air Force’s ABMS is likely to result in value in many conceivable ways, but perhaps not in JADC2. This is a problem since at a fundamental level the Department is organized to execute programs through the services. The risk of Conway's Law manifesting in JADC2 is that each service's efforts will prioritize their own unique requirement and communication structures, leading to a disjointed or... non-joint solution. This would undermine the very essence of JADC2, which aims to provide a unified and integrated command and control system across all domains and services.

It seems like DepSecDef Hicks realizes this possibility and has therefore taken on an outsized role with the AI and Data Accelerator Initiative (ADA) and the Chief Digital AI Office (CDAO). We’re also seeing CCMD commanders realize that JADC2 comes together at the CCMDs or not at all - hence the big investments being made by Gen Kurilla with CENTCOM’s Digital Falcon Oasis and by Gen VanHerck with NORTHCOM’s Global Information Dominance Exercise (GIDE). That JADC2 likely requires a CCMD-centric approach is further reinforced by DIU’s focus on delivering capabilities to the CCMDs via its Hedge portfolio. Chris Brose’s Moneyball Military paper argues that, “…the services cannot monopolize the government’s role in the supply of military power. More of that function must be shifted to alternative institutions that report to the Pentagon’s top leaders, to whom they should be accountable for disrupting the status quo and supplying Moneyball capabilities directly to military consumers.”

If the services are going to drive this, to avoid the pitfalls of Conway's Law and ensure a truly joint JADC2, the focus should shift from individual service efforts to a Combatant Command (CCMD) centric approach. This would involve each service's JADC2 efforts competing against one another to win the support and endorsement of each COCOM. By doing so, the services would be incentivized to prioritize joint requirements and interoperability, ensuring that the final JADC2 solution is truly integrated and capable of meeting the needs of all stakeholders.

This COCOM-centric approach would not require a complete reorganization of the DoD, as a Goldwater-Nichols 2.0 might. We have two options:

1. Leverage the existing organizational structure and promote healthy competition between the services (with funding tied to what COCOM commanders chose to fight with) to achieve the desired joint outcome. Each service has to win all the Components and the CCMD itself at a given COCOM.

2. Decide that the software for a joint force can’t be built through the services due to Conway’s law and charge DIU or CDAO to build and deliver this. The ideal outcome would have at least two competing efforts. Over time the services will retain equipping their forces in all but software. In software they will compete against each other to break the DoD monopsony and drive continuous competition and speed of war innovation.

Reorgs cause a lot of brain damage, confusion, and wasted time, when we have none to waste. JADC2 is fully realizable if instead of reorganizing our Department, we reorganize around the problem.