by DR. Ricardo Petrissans | Nov 23, 2025 | Fight Against Technological Monopolies, States and technology
A narrative chronicle about software that turns artillery flashes into lines of code.
Night has closed over Donetsk oblast. In a makeshift command post, a Ukrainian officer holds a dust-covered tablet while a kamikaze drone hums outside. On the screen, a multicolored mosaic appears: red dots that may be enemy artillery pieces, blue arrows marking civilian evacuation routes, and a blinking counter that reads: Latency = 2.7 s.
Minutes earlier, a commercial satellite captured a burst of images. Seconds later, Gotham correlated them with radio intercepts and Telegram warnings. The officer draws a circle, taps confirm, and, almost without hearing his own order, watches an allied shoot-and-scoot battery fire at the newly marked grid.
This is the eighth chapter of the story: the war of the twenty-first century, where shots still smell of powder, but the decisive movement happens inside GPU clusters hundreds of kilometers away from the front.
The Algorithmic Dawn: Ukraine 2022, the Unscripted Demo
When Russia crossed the border, Kyiv had no time for procurement rituals. Palantir arrived with backpacks and a motto: integrate first, invoice later. In less than ten days, its engineers connected DJI drone feeds, Starlink satellites, AN/TPQ-36 radar, and threads from social networks.
The promise was to fuse everything into a graph that could show, at village scale, who was firing, from where, and with what probability of moving five minutes later. One colonel later remembered that wall of screens: serial number of a tank, estimated GPS position, fuel level. It was like seeing war in Google Maps, but alive.
General-staff manuals became obsolete before they could be printed. The battlefield was no longer only a line of contact; it was a living data structure.
The OODA Loop at Fiber Speed
Strategists once spoke of the OODA loop, Observe, Orient, Decide, Act, as the compass of modern combat. With Gotham, that cycle contracts into three verbs: observe, act, learn. Orientation is automated; decision, often enough, is compressed into a tap on a screen.
A Polish commander involved in training put it bluntly: some platoons fire because the tablet paints the target red; human doubt becomes latency. The danger is obvious. If the enemy injects false data, the graph can transform a mirage into a legitimate target.
To reduce that risk, Palantir added visible confidence scores, color-coded like traffic lights, and a delay button that forces a short pause before fire is launched. But under the roar of drones, the border between reflex and reflection is thin.
Low-Cost Drones and the Sensor Mesh
Ukraine combined Turkish Bayraktars with quadcopters that cost less than a laptop. Each device uploaded telemetry to Foundry; Apollo deployed patches that refined armored-vehicle detection models in hours instead of weeks.
When a cheap drone recorded enemy movement, the video was fragmented, sent through Starlink, reconstructed in a European data center, and pushed to an artillery operator before the drone returned to base. The secret was not only the drone. It was the software that coordinated a swarm of cheap eyes.
The Peaceful Theater: NORD-24 in Norway
In 2024, NATO simulated an Arctic invasion in the NORD-24 exercise. Gotham fused coastal radar, maritime AIS, and temperature sensors in pipelines. When a ghost vessel switched off its transponder, the system reconstructed its path from thermal wakes and cell-phone pings caught by coast-guard antennas.
A Norwegian admiral said it felt like catching a submarine with a net made of data. Critics heard a different alarm: if this level of tracking is justified for defense, how easily can civilian life be patrolled in the same name? Brussels asked for reports; NATO promised proportional use.
War as a Service
Palantir has often preferred value contracts over traditional licenses: payment linked to reduced allied casualties or to the neutralization of enemy systems. In Ukraine, part of the compensation was tied to the precision of HIMARS operations supported by Gotham.
A London think tank warned that monetizing targets creates perverse incentives: war becomes scoreable. Palantir answered that outcome-based pricing aligns cost with lives saved. The debate moved to the United Nations, but consensus did not arrive before the artillery shells did.
Generative AI in the General Staff
With AIP-Defense, officers could ask in ordinary language: what if the enemy cuts the M-03 highway after Izium? The model generated alternative routes, logistics-wear curves, and drafts of operation orders. One captain described it as an invisible staff major.
Yet the model could hallucinate. One day it proposed crossing a bridge that friendly artillery had destroyed weeks earlier. Since then, AIP displays a lag counter: how many minutes have passed since the last satellite verification. Even imagination must carry a timestamp.
Cyber and Kinetic: The Double Helix of Conflict
In February 2025, a Russian wiper virus took down municipal servers in Odesa. Gotham detected the spike in malicious hashes, crossed IP addresses, and linked coordination patterns with ISR drones circling the city.
The correlation suggested an imminent artillery strike. Three blocks were evacuated. The bombardment fell where the disks were already dead. Bits and bullets converged. A U.S. colonel coined the term kill-byte: hard drives are struck to guide projectiles.
From Kabul to Kinshasa
After Afghanistan, several allied commands kept tablets with Gotham preloaded. In 2025, UN Blue Helmets deployed in eastern Congo needed to protect humanitarian convoys. Palantir offered a disarmed version: it did not mark targets, only ambush alerts.
NGOs welcomed fewer attacks; academics feared first-class data regions surrounded by analog darkness. The company insisted that the tool does not kill, it only illuminates. But on the ground, every map is power.
Algorithms and the Law of War
The International Criminal Court began asking whether Gotham captures could be admissible evidence in war-crimes cases. The difficulty lies in inference. If a system labels a vehicle as probable T-90M, is probable enough to convict a commander? Lawyers now answer with their own confidence scores.
Palantir experimented with forensic mode: graphs are frozen, hashes registered, chain of custody preserved. It is a race against volatility, because digital truth ages at the speed of cache.
Epilogue: Phosphorus Flashes, Silicon Glints
Back in Donetsk, the officer watches the smoke dissolve where a rival battery had been alive a minute earlier. He wonders whether wars are won by courage or computation. In Denver, a GPU-usage graph loses a peak; in Kyiv, a mother receives a message from her son: I am fine.
Between those two ends, war becomes an equation close to real time. Palantir sells millimetric certainty in a profession built on chaos. But every reduction in latency evaporates the interval where mercy can still fit. Perhaps the next version needs not only a fire button, but a doubt button.
by DR. Ricardo Petrissans | Nov 16, 2025 | Fight Against Technological Monopolies, States and technology
Aboard a C-130 flying over Helmand province, an infantry captain reviews reports printed in haste. Each page carries a QR code: when scanned, his tablet opens a map where dusty roads flash red, yellow, or green according to the probability of improvised explosive devices.
The person coloring those routes does not wear a uniform. He works thousands of miles away, in an ordinary office in Denver, and connects to the same database that feeds the troops. Behind the screen is a platform called Gotham, and behind Gotham lies a question: how much power should a state delegate to algorithmic scoring in war?
The baptism of fire: Afghanistan
Palantir’s first contracts with the U.S. Department of Defense seemed modest: pilot licenses to help forensic analysts find patterns in IED detonations. By 2010, however, Gotham was being deployed close to the field, ingesting patrol records, incident reports, and intercepted communications to anticipate where danger might appear.
The predictions were not infallible oracles, but they forced routes and schedules to be reconsidered. The platform became part of operational planning in Afghanistan and later Iraq: intangible, but present in the way decisions were made.
From Mosul to Los Angeles
In 2016, the battle for Mosul tested the flexibility of the software. Drone videos, street names, infrastructure maps, and field reports could be layered into one operational picture. Gotham functioned like a hybrid brain, absorbing urban chaos and returning routes with lower collateral risk.
That same logic also migrated into domestic policing. In Los Angeles, predictive-policing programs used data systems to identify hot spots and risk profiles. Civil-liberties groups criticized the projects, arguing that tools born for war can distort community policing when used without transparency and accountability.
Interagency intelligence
In national-security rooms, the same graph may display logistics flows, financial movements, intercepted conversations authorized by courts, cyberattack indicators, and public-health data. A senior official can change layers with a finger, moving from narcotrafficking to ransomware or pandemics.
The problem is that many decision-makers do not understand the code that prioritizes one lead over another. They trust the briefings and the audit trail, but democratic accountability requires more than trust in a screen.
Health, borders, and war
During the COVID-19 crisis, data platforms were used to coordinate hospital capacity, protective equipment, ambulance telemetry, and supply chains. Supporters argued that this saved lives; critics replied that emergency usefulness does not erase concerns about storing sensitive health data in private systems.
In immigration enforcement, Palantir-linked tools have been criticized for connecting detention databases, driver-license records, and social-media information. In Ukraine, the company’s systems reportedly helped fuse satellite images, drone videos, and open-source intelligence into faster operational cycles. Each case shows the same pattern: scattered data becomes an action map.
Transparency, dependence, and ethics
European regulators and civil-society organizations have pushed for stronger oversight of systems used by law enforcement and defense. Palantir argues that full openness could expose tactics to adversaries. Critics respond that black boxes are incompatible with democratic control.
There is also the issue of dependence. If a state builds critical capabilities on a private platform, what happens if prices rise, licenses change, or access is disrupted during a crisis? Strategic lock-in is not only economic; it can become tactical.
Conclusion
Palantir turns dispersed data into maps of action. The benefits are visible: fewer operational blind spots, faster logistics, better coordination. The risks are equally visible: opacity, dependency, civil-rights concerns, and the temptation to let a graph speak louder than human judgment.
The question is no longer whether states will use such platforms. The real question is under what conditions they can keep using them without compromising the delicate pact between security and freedom.
by DR. Ricardo Petrissans | Nov 9, 2025 | Fight Against Technological Monopolies, States and technology
As Palantir Technologies established itself as a key player in the intelligence sphere, its Gotham platform became an indispensable tool for national-security agencies and law-enforcement bodies.
This chapter focuses on how Palantir’s technology transformed national security, addressing its use in crucial operations, its impact on decision-making, and the ethical implications that arise from its application in surveillance contexts.
Gotham’s efficiency in the fight against terrorism
Since its implementation, Gotham has proven to be a powerful tool in counterterrorism. The platform’s ability to integrate and analyze large volumes of data has allowed analysts to identify threats more effectively. By combining information from multiple sources, Gotham provides a holistic view of a situation and helps detect patterns and relationships that would not otherwise be visible.
One of the most cited examples of Gotham’s effectiveness is its alleged role in operations that helped track networks linked to Osama bin Laden. Through data analysis, analysts could map support networks, identify collaborators, and locate relevant patterns. This type of operation illustrates how technology can be used to save lives and prevent attacks.
The expansion of Gotham in security work
As intelligence agencies began trusting Palantir, Gotham expanded into other areas of national security. Law-enforcement bodies used it to solve crimes, monitor illicit activity, and manage real-time operations. This diversified use allowed agencies to make better-informed decisions based on updated information.
In the field, real-time access became essential. During operations, analysts could consult updated information about suspicious activity and act faster. In contexts where time is decisive, such as identifying and dismantling terrorist cells, that capacity can change outcomes.
Successful applications and crisis management
Beyond counterterrorism, Gotham has been used in operations against organized crime, drug trafficking, and crisis response. By integrating reports, geolocation, communications, and other sources, the platform can help agencies coordinate action and allocate resources more effectively.
During natural disasters, emergency agencies have used data platforms to coordinate responses and optimize the distribution of resources. The ability to combine weather reports, infrastructure status, and victim-location information can support faster and more informed decisions.
Technology and decision-making
Palantir’s technology did not merely improve operational efficiency; it also changed the way decisions are made. Real-time analysis shifted organizational culture toward a more proactive model of threat identification and mitigation.
Yet this transformation raises questions about overdependence on technology and potential bias in algorithmic systems. When data platforms become central to decision-making, institutions must ask who defines the categories, how errors are corrected, and what accountability exists when a decision harms individuals or communities.
Surveillance, ethics, and accountability
As Palantir consolidated its leadership in data analysis, criticism intensified. Civil-rights organizations and privacy advocates questioned the ethics of surveillance enabled by Gotham. The ability to integrate data from multiple sources has generated concerns about mass surveillance and possible abuse of power.
A major concern is opacity. If the algorithms and data workflows used by agencies remain hidden, citizens may not know how decisions affecting their lives are made. This is especially sensitive when surveillance targets minority communities or when predictive systems reproduce historical bias.
Palantir argues that its software is a tool and that final use depends on public policy and institutional safeguards. Critics respond that technology providers cannot entirely separate themselves from the consequences of systems they design, deploy, and maintain.
Conclusion
Palantir’s technology has transformed the way intelligence agencies and law-enforcement organizations approach data gathering and analysis. Gotham’s capacity to integrate information in real time has improved operational efficiency, but it has also raised difficult questions about surveillance, transparency, and responsibility.
The central challenge is not only whether the technology works. It is whether democratic societies can govern such tools without sacrificing civil rights in the name of efficiency and security.
by DR. Ricardo Petrissans | Nov 2, 2025 | Fight Against Technological Monopolies
A story told from cables and lines of code.
Most data-analysis platforms present themselves as multi-purpose Swiss Army knives. Gotham, Foundry, and Apollo prefer the role of a symphony orchestra. Each instrument has its part, but the sound becomes complete only when all three share the same score.
In this chapter of the series, we open the metal covers of the servers and step inside the processes that sustain the Denver giant. The journey is not for the faint-hearted: there are still-warm screws, armored network routes, and a handful of half-written secrets.
Imagine a room without windows, lit only by the pale glow of monitors. In the shadows, an analyst types the license plate of an ordinary car. She does not know exactly what she is looking for; she only senses that the sequence might be the loose thread leading to a drone-parts shipment or simply a human error.
What she does know is that when she presses Enter, an enormous invisible mechanism begins to move. That mechanism is Gotham, acting like a sleepless detective: smelling records, capturing coordinates, crossing pieces of information that do not yet have names, and returning connections nobody would have guessed otherwise.
Gotham: the vigilant engine
Gotham was born from the frustration of analysts who had notes but no corkboard and pins. It was built so facts—people, places, serial numbers, blurry images—could come alive inside a vast graph where everything relates to everything without losing traceability. Every click is recorded so that, years later, a judge can reconstruct who saw what.
Gotham does not dream. It keeps watch.
Foundry: the industrial foundry of data
The world, however, does not end in government corridors. Engineers who fed Gotham began receiving messages from companies that had nothing to do with espionage. Airbus wanted to reduce maintenance delays; Merck needed to accelerate clinical trials; luxury carmakers wanted to tame oceans of telemetry.
Foundry was born as a forge where corporate silos could melt and take a new shape. For a newcomer, Foundry feels less martial than Gotham. There are no colored security badges or confiscated phones. Instead, there is a blank canvas where Excel sheets, CSVs, sensors, orders, and supply chains become living entities.
When everything is aligned, Foundry does not need spectacle. It convinces by showing tangible savings, weeks gained on the calendar, and arguments finally settled through shared evidence.
Apollo: the invisible conductor
There was still an obstacle: updates. Software changes every day, but Palantir’s clients often operate in isolated networks, sometimes underground or under the ocean. How do you send a critical patch to a nuclear submarine without opening a hole in its digital shield?
Apollo emerged from that question. Think of it as an invisible conductor. When a developer commits code, Apollo compiles, signs, packages, and deploys the new version according to the destination environment. In the cloud it travels through fiber; in classified data centers it may move through guarded transfers; in disconnected environments it arrives slowly but intact.
The AI layer
When large language models entered the scene, Palantir presented them as a new guest at the party. The idea behind AIP was simple but powerful: allow an analyst to ask in ordinary language and receive SQL, Python, graphs, or operational answers without violating any confidentiality label.
The leap seems modest until one sees it in action: logistics routes optimized in seconds, links between legal documents emerging like fireflies, reports generated on the fly and ready for regulatory review.
The unresolved debate
These benefits come with difficult questions. How trapped does a client become inside an ecosystem whose ontologies only the provider truly understands? What happens when an algorithm designed to prioritize ambulances is used with a different data set to track protesters? How much transparency is possible without exposing state secrets?
Today, with one foot in public contracts and another in private operations, Palantir sees itself as a provider of sovereign data infrastructure. Its critics prefer to call it a gatekeeper. Whatever term prevails, Gotham, Foundry, and Apollo have shown that the same architecture can move from the war room to the factory floor without changing its core logic.
by DR. Ricardo Petrissans | Oct 26, 2025 | Fight Against Technological Monopolies, Technofeudalism
A narrated chronicle.
It was the spring of 2003 in Palo Alto. In the backyard of a Victorian house, five young founders gathered daily with the feeling that the world had changed forever after September 11. Peter Thiel, recently out of PayPal and fascinated by Tolkien, insisted that the key to preventing the next attack was not more cameras or higher walls, but patiently weaving together the web of data that already existed, scattered across incompatible silos.
Around a folding table, with open laptops, cold pizza, and the smell of California pine, Stephen Cohen sketched graphs of nodes and edges on a whiteboard. Alex Karp, a philosopher with a sharp tongue, questioned every concept aloud, while Joe Lonsdale and Nathan Gettings played chess with SQL queries as if they were pawns.
The company’s name came from Thiel almost as a joke: Palantir, the seeing stones of Middle-earth. Everyone laughed and nodded, unaware that the word would one day trade on Wall Street.
The first secret ally
The founders worked behind closed doors. They needed to design software capable of integrating flight records, call transcripts, banking transactions, and intercepted messages, all with an audit level strong enough to push back against the ghost of Big Brother.
Two years later, a call from McLean changed the story: In-Q-Tel, the CIA’s venture-capital arm, offered funding and, more importantly, access to real intelligence analysts. From 2005 onward, Palantir engineers began coding inside closed rooms where phones stayed outside and windows were covered.
The challenge was brutal: classified data could not leave secure networks, but the software had to evolve daily. That pressure gave birth to one of Palantir’s defining disciplines: forward-deployed engineering, developers who spend weeks or months embedded inside the client’s environment.
Gotham, the first great leap
In 2008, that permanent dialogue produced a product with its own name: Gotham. It allowed analysts to trace connections through drag-and-drop interfaces instead of endless scripts. Every click was logged, every piece of information tagged according to secrecy level. What began as a tool to hunt terrorists soon helped solve financial crimes and human-trafficking networks.
The reputation spread through Washington. The FBI, NSA, Department of Defense, and other agencies wanted to test the software built by outsiders who seemed to understand their real problems.
A foot outside the state: Foundry
With public-sector clients expanding, Palantir faced a new question: what about the private sector? Many employees wanted to apply the same technology to ordinary but complex problems: manufacturing delays, pharmaceutical traceability, delivery routes, fleet maintenance, and industrial operations.
Foundry emerged as a command cabin for companies. Airbus used it to optimize fleet maintenance; Merck used it to shorten clinical trials; Ferrari used it to read telemetry in real time. The culture, however, remained similar: whole teams lived inside the client’s plant as if every factory were a new military base.
The lights of Wall Street
Seventeen years after that cold pizza in Palo Alto, Palantir went public through an unusual direct listing. Alex Karp spoke from Colorado, reminding investors that the company had taken nearly as long to go public as Apple took to launch the iPhone.
The market was skeptical at first, but the stock gained momentum as the company signed contracts tied to measurable savings. By 2024, Palantir announced its first full year of GAAP profitability and a strong cash position with no relevant debt.
Apollo and the AI era
Managing software deployed across secret servers, public clouds, and even submarines required a new layer. Apollo made it possible to update Gotham and Foundry without shutting down the machine, even in disconnected or classified environments.
When large language models began dominating headlines, Palantir responded with its Artificial Intelligence Platform. The promise was not cosmetic chatbots, but the ability for analysts to ask questions in ordinary language and receive SQL, Python, maps, or operational plans while respecting every classification level.
Shadows and dilemmas
Palantir’s rise has not been pure glory. Gender-discrimination lawsuits, work with ICE, and accusations of algorithmic opacity have kept the company under scrutiny. Karp often answers that neutrality does not exist and that the company chooses the clients it believes in. That statement captures the ethical dilemma: can a piece of software be patriotic while protecting everyone’s freedoms?
If Palantir can sustain the balance between sovereign data infrastructure and democratic oversight, its story may still be beginning. If not, the same doors that opened so quickly may close with equal force.
by DR. Ricardo Petrissans | Oct 19, 2025 | Fight Against Technological Monopolies, Technofeudalism
In an increasingly interconnected world, where information has become the most valuable resource, technology companies have emerged as new titans of global power.
Among them, Palantir Technologies stands out not only for its innovative technology, but also for its role at the intersection of surveillance, security, and politics.
Founded in 2003 by a group of visionaries, including Peter Thiel, Palantir has traveled a fascinating path from modest beginnings to becoming a crucial actor in data collection and analysis. But its trajectory has not been free of controversy or criticism, and its evolution reflects a broader change in the way technology companies influence our lives and global governance.
This article offers a broad exploration of Palantir, its programs, its evolution, its relationship with other technology companies, and its impact on contemporary society. By the end of this journey, readers will better understand how Palantir has placed itself at the center of the conversation about privacy, security, and corporate power in the twenty-first century.
The beginnings of Palantir
The story of Palantir begins in the post-September 11 context, a period in which United States intelligence and security agencies were looking for new ways to fight terrorism and protect the nation.
Founded in 2003, Palantir was conceived as a data-analysis tool designed to help government agencies identify patterns and connections among data that would otherwise have remained hidden.
The vision of Thiel and his cofounders was to create software capable of integrating and analyzing large volumes of information from diverse sources, from government databases to intelligence reports. This innovative approach promised to improve the capacity of security agencies to prevent attacks while also creating an attractive business model: working closely with government and security agencies while expanding toward commercial and corporate sectors.
Palantir initially launched with three key products: Palantir Gotham, focused on national security; Palantir Metropolis, aimed at financial institutions; and Palantir Foundry, which allows companies to manage and analyze their own data. This diversification showed the adaptability of the company and positioned it to play a fundamental role in data management across many sectors.
The technology behind Palantir
Palantir’s technology is based on the idea that true intelligence is not found merely in collecting data, but in the ability to analyze it and extract meaningful conclusions.
Its software uses advanced techniques in data analysis, machine learning, and visualization to allow users to explore and understand complex networks of information. Gotham has been used by intelligence agencies and security forces around the world. Its ability to integrate criminal records, intelligence reports, social networks, and other sources helps analysts identify behavioral patterns and connections that may be crucial for crime and terrorism prevention.
Foundry, meanwhile, found its place in the private sector, where companies seek to use their data to improve efficiency and decision-making. It allows organizations to integrate internal and external data, identify opportunities, and optimize operations in energy, health, manufacturing, and other sectors.
In 2020 Palantir introduced Apollo, a platform that allows organizations to deploy and manage data applications anywhere, whether in the cloud or on local servers. Apollo reflects the growing trend toward cloud computing and the need for flexible solutions adapted to changing business and government demands.
Expansion and strategic collaborations
As Palantir consolidated its place in the market, it began forming strategic alliances that expanded its reach and capabilities. Since its origins, the company has been closely linked to U.S. government agencies such as the Department of Defense, the FBI, and the CIA, providing tools that allow them to analyze information more effectively.
These partnerships have also raised concerns. Palantir’s ability to access and analyze sensitive data has led civil-rights activists and privacy groups to warn that this technology can enable abuse and erode civil liberties.
Palantir has also formed alliances with private companies such as IBM, Amazon Web Services, and Microsoft, allowing it to integrate its software into a variety of platforms and offer more robust solutions to clients.
Controversies and criticism
Despite its success, Palantir has been surrounded by controversy. Concerns about privacy, surveillance, and the use of its technology have fueled an intense debate about the role of technology companies in society.
The capacity to analyze massive volumes of data raises ethical questions. Activists argue that Palantir’s technology can be used for mass surveillance and may violate citizens’ rights. Palantir defends its approach by arguing that its mission is to help agencies prevent crime and improve national security.
Criticism has continued, especially around transparency. Many argue that the company should be more open about how data is used and how its products are implemented by government agencies. Palantir has responded by emphasizing its commitment to ethics and privacy, but the tension between security and individual rights remains central to the future of data technology.
Conclusion
Palantir Technologies has traveled a fascinating path from its beginnings to becoming a key actor at the intersection of technology, surveillance, and politics. Its evolution reflects not only the growth of one company, but also larger changes in the way technology firms influence our lives and global governance.
As society navigates a more complex landscape, where data and technology play a central role, Palantir’s story reminds us of the challenges and opportunities ahead. The balance between innovation and ethics, security and privacy, will be decisive in shaping the future of technology and its impact on society.
