by DR. Ricardo Petrissans | Dec 21, 2025 | Fight Against Technological Monopolies, Technofeudalism
A narrative chronicle about software that dissects pandemics, genomes, and health economies.
At dawn in the basement of the Pasteur Institute in Dakar, virologist Oumar Ndiaye drops a reactive strip onto a steel table. Seconds later, her laptop vibrates: Foundry has finished aligning 63,000 viral sequences from five continents.
The algorithm marks in red a mutation pattern that may increase the virus’s affinity for lung receptors. Oumar drinks coffee and presses alert. In Stockholm, the warning arrives at the ECDC with a critical-priority label. An invisible thread connects two laboratories and shrinks the world into a microbiologist’s screen.
The Immunological Baptism: COVID-19 and the Dashboard that Never Slept
In April 2020, the United Kingdom created the NHS Data Store with Foundry as one of its engines. What began as a count of beds and ventilators became an almost organic map of the health system: ICU lists, dexamethasone stocks, oxygenation rates by municipality.
Each morning, a priority algorithm reordered ambulance movements; each night, it proposed transfers to avoid saturation. The success opened the door for Palantir in applied epidemiology, but the ethical debate was only beginning.
Genomes as Passports
In Iceland, a sequencing company uploaded hundreds of thousands of genomes to correlate rare variants with cardiovascular risk. The government imposed two locks: full anonymity and a veto on police use. Foundry generated maps of risk alleles and linked them with diet, address patterns, and environmental exposure.
Doctors saw new possibilities. Critics saw a map that could be used to discriminate in life insurance or employment. Genetic geography became both a medical tool and a political warning.
Clinics on Autopilot
At a major clinic in Rochester, a digital twin of hospital operations predicted bottlenecks in operating rooms and optimized schedules. A resident could ask how much time would be saved by switching two procedures, and Foundry could calculate the operational impact.
Efficiency impressed administrators until clinicians noticed that certain patients, especially those with weaker insurance coverage, were being pushed toward less desirable time slots. The administration called it resource optimization; unions saw socioeconomic bias.
Pharmaceutical Companies and Orphan Molecules
Several pharmaceutical and biotech firms explored blind data consortia with Palantir to accelerate orphan-drug development. Each company contributed encrypted preclinical data; Foundry looked for statistical patterns without exposing proprietary secrets.
One correlation between a kinase inhibitor and a rare childhood cancer appeared in days. The protocol draft followed quickly. The legal question was harder: if everyone contributed a piece of the discovery, who owns the molecule and who pays for the trial?
Epidemiological Surveillance
In Singapore, dengue-control sensors collected data from urban planters. Gotham integrated larval counts, sales of repellent, social-media posts mentioning fever, and construction permits. The system suggested fumigating two blocks and delaying a metro project.
The contractor protested the cost of delay. The city obeyed the software. Public health gained a new radar, but governance gained a new problem: how much economic activity can an algorithm interrupt in the name of prevention?
Precision Nutrition and Digital Trials
In São Paulo, a health app could cross a person’s medical history, voluntary genotype, and the sodium content of processed food to produce a red, yellow, or green signal. The line between helpful nudge and coercive diet policy became difficult to draw.
In Oxford, a trial for multiple sclerosis recruited patients through a mobile application. Foundry assigned treatment or placebo according to genomic strata and saliva biomarkers. The adaptive design accelerated the process, but it also risked excluding mixed-ancestry participants as statistical outliers.
Biosecurity and the Ethics of the Graph
As gene editing became cheaper, biosecurity entered the same data universe. A Gotham-BioShield module could track DNA-synthesizer orders and cryptocurrency payments. Supporters saw protection; critics feared confusion between legitimate research and bioterrorism.
In transplant systems, graphs could expose organ-trafficking patterns while also revealing protected donor identities. The paradox is stark: the graph that uncovers corruption can also expose medical secrets.
Epilogue: Silicon Heartbeats, Human Pulses
Back in Dakar, Oumar’s alert triggers a call linking Stockholm, Atlanta, and Canberra. Ten minutes later, airports adjust procedures, veterinarians inspect farms, and the World Health Organization drafts a cautious statement.
Public health enters an era where digital agility can outrun political inertia. Palantir sells speed; states buy hope. But every line of code that helps cure can also classify, segregate, or expose. When biology is written in SQL, a misplaced parenthesis may decide who gets to breathe tomorrow.
by DR. Ricardo Petrissans | Dec 14, 2025 | Fight Against Technological Monopolies
As Palantir Technologies consolidated itself as a key actor in intelligence and security, its founders saw an opportunity to diversify. In 2015, the company launched Palantir Foundry, a platform designed to help organizations manage their own data.
This chapter focuses on Palantir’s expansion into the private sector, how Foundry changed the way organizations manage and analyze information, and the criticisms that accompanied that expansion.
The Creation of Palantir Foundry
Foundry was a strategic step. It allowed Palantir to move beyond government clients and enter corporate operations, health care, finance, manufacturing, logistics, and research. The platform was designed to integrate, visualize, and analyze data without requiring every user to be a specialist.
Its interface promised accessibility. Users could create customized analyses and applications without advanced programming skills. That democratization gave organizations a way to turn scattered databases into operational insight.
Success Cases in the Private Sector
Since its launch, Foundry has been adopted by companies in multiple sectors. In health care, hospitals and public-health organizations used it to manage patient information, optimize the supply of medicines, and analyze outbreaks.
During the pandemic, New York City’s health administration used Foundry to track the virus and coordinate responses in real time. The platform integrated information from hospitals, laboratories, and agencies, giving authorities a more complete picture of pressure across the system.
In finance, Foundry was used to detect fraud and manage risk. By integrating data from different sources, institutions could obtain a more holistic view of operations. Investment firms also used the platform to analyze behavioral patterns and optimize strategy.
Digital Transformation inside Companies
Foundry accelerated digital transformation because it turned data into something teams could use collaboratively. Instead of treating information as isolated files locked inside departments, organizations began to treat data as a shared operational asset.
This changed decision-making. Managers could see supply chains, risk factors, and customer behavior in the same environment. Collaboration became easier because different departments were no longer arguing from incompatible spreadsheets.
Challenges and Criticism
The success of Foundry did not eliminate criticism. Corporate data analysis raises questions about privacy, consent, and the limits of optimization. Organizations must ensure that data collection is transparent and justified.
Critics also point to the opacity of algorithmic systems. If decisions are based on Palantir’s models, how can employees, consumers, or regulators understand the assumptions behind them? The problem is not only technical; it is institutional.
The Ethics of Data Management
Palantir’s expansion into the private sector intensified the wider debate about data ethics. Companies increasingly use analytics to optimize operations, but efficiency can become dangerous if privacy and rights are treated as secondary constraints.
Transparency and accountability are essential to maintaining trust. Companies that deploy platforms like Foundry need clear rules about what data is collected, who can access it, how long it is retained, and which decisions it is allowed to influence.
Conclusions
Palantir’s expansion through Foundry transformed the company’s identity. It was no longer only a government intelligence contractor; it became a provider of infrastructure for corporate decision-making.
Foundry has shown value in many sectors, but it also presents significant questions about ethics and transparency. The central question remains: how can organizations benefit from data integration without giving up oversight, accountability, and human judgment?
by DR. Ricardo Petrissans | Dec 7, 2025 | Fight Against Technological Monopolies, Technofeudalism, Transhumanism
The COVID-19 pandemic was an unprecedented event in recent history. It affected millions of people and placed health systems under a pressure few institutions had imagined. In that context, Palantir Technologies became a relevant actor in data management and crisis response.
This chapter looks at how Palantir, especially through Foundry, helped governments and health organizations respond to the pandemic, and at the ethical and social questions raised when public health is organized through private data infrastructure.
The Need for Rapid and Effective Responses
As COVID-19 spread, governments and health organizations urgently needed to collect and analyze data to understand the movement of the virus. The lack of precise, real-time information complicated containment efforts.
Foundry was used to integrate data from hospitals, laboratories, and public-health agencies. That integration allowed authorities to identify outbreaks, assess hospital capacity, and coordinate responses with more speed than traditional reporting systems allowed.
Use Cases During the Pandemic
One prominent case was the implementation of Foundry by the U.S. Department of Health and Human Services. The platform helped track the spread of the virus and manage the distribution of resources such as tests, protective equipment, and medical supplies.
By integrating data on hospital capacity, PPE availability, and testing demand, public officials could make more informed decisions about resource allocation. Data visualization became a way of seeing pressure points before they turned into collapse.
Another crucial application was vaccine distribution. Palantir helped coordinate deliveries and identify vulnerable populations by combining demographic information, infection rates, and vaccine availability. In a campaign defined by scarcity and urgency, logistics became a public-health instrument.
Collaboration with Health Organizations
Palantir did not work only with the federal government. Hospitals and local health systems also used Foundry to manage patient data and coordinate crisis response. In cities such as New York, the platform helped share information on bed capacity, oxygen availability, and other critical resources.
That collaboration highlighted the role technology can play in health emergencies, but it also raised concerns about privacy, consent, and the handling of sensitive medical data. The same integration that saves time can also centralize risk.
Ethical and Social Implications
As Palantir became involved in pandemic management, ethical questions intensified. Health data is among the most sensitive forms of personal information. How can authorities guarantee responsible use? What safeguards protect patients from misuse or later repurposing of their records?
The lack of transparency around how data is processed has generated concern about power, bias, and accountability. These concerns are especially significant for minority communities, which can be disproportionately affected by automated resource allocation or public-health decisions.
Technology used to manage health crises also raises questions of equity. If data-driven allocation becomes the norm, vulnerable communities must not be left invisible because their data is incomplete, delayed, or poorly represented.
The Responsibility of Technology Companies
Palantir’s participation in the pandemic response underscores the responsibility of technology companies in public health. When private platforms become essential to crisis management, ethical frameworks cannot be an afterthought.
Transparency, accountability, and clear limits on data use are central. Palantir has promoted guidelines for responsible technology, but the effectiveness of those measures depends on the institutions that deploy the tools and on independent oversight.
Conclusions
COVID-19 challenged health systems around the world. In that environment, Foundry proved valuable for integrating information, accelerating decisions, and coordinating resources. Its use showed that data infrastructure can become part of the emergency response itself.
At the same time, Palantir’s role opened difficult questions about ethics, privacy, and responsibility in the collection and use of health data. The next challenge is not only to make health systems faster, but to ensure that speed does not come at the cost of rights, fairness, or public trust.
by DR. Ricardo Petrissans | Nov 30, 2025 | Fight Against Technological Monopolies, Technofeudalism
A narrative chronicle about the conquest of the private sector.
A process engineer in a Duisburg steel mill ends his shift with rust on his hands. Before hanging up his helmet, he looks at the screen above the continuous casting line: the scrap rate has fallen from 12 percent to 4 percent in six months.
Nobody asked him to memorize that number, but he does. Behind the drop lives a piece of software that has never set foot in the mill, a system the workers now call, half mockingly, the oracle. Its official name is Foundry, and it carries Palantir Technologies’ signature.
The Civil Baptism: Airbus and the Theory of Lost Minutes
In a hangar at Toulouse-Blagnac, an A350 waited for release. The obstacle was not a mechanical failure but two special nuts whose SKU existed only in a lost spreadsheet. Someone suggested trying the thing the CIA uses. A small Palantir team arrived with carry-on bags and the caution of people used to uncomfortable questions.
The test was straightforward: connect inventory, logistics, and aircraft telemetry in one control panel. The metric made the case. Every minute an aircraft does not fly costs money in fixed expenses and in lost opportunity. Airbus renamed the project Skywise and tied payment to reduced delays. It worked. The backlog began to dissolve.
Steel, Sensors, and the Smell of Gas
The steel mill was not alone. In Ohio, an automotive plant connected a thousand legacy PLCs with forty years of defective-parts history. Foundry found strange correlations: humidity spikes in secondary storage corridors predicted defects in door polishing.
Foundry’s Frankenstein ontology, stitching SAP, CSV files, barcode photos, and machine signals, did not distinguish between a noble data source and a humble anecdote. It aligned them in time and let anomalies jump out. The lesson repeated itself: a rough insight today beats a beautiful dashboard after production has stopped.
Wall Street Raises an Eyebrow
When Palantir went public in 2020, analysts struggled with a billing model that seemed written by playwrights rather than accountants. There were no simple perpetual licenses. The company favored as-you-save contracts, percentages of generated value, and, when needed, equity in start-ups.
Some audit committees refused to sign because they could not predict the invoice. Later, several returned quietly after competitors reduced inventory cycles and displayed those savings in earnings calls. The question changed from why pay for this? to can we still enter the algorithm club?
The Rise of the Chief Data Officer
Boards did not want graph jargon or ETL diagrams, so they adopted an easier phrase: Palantir is the command console. With that, the Chief Data Officer moved closer to the center of corporate power.
In one Swiss pharmaceutical company, the CDO obtained veto power over mergers if data silos could not be integrated within a quarter. Balance sheets were joined by a new line item: cost of ontological alignment. Investors noticed. Data architecture became a form of operational insurance.
Apollo in Hostile Territory
The private sector discovered not only Foundry but Apollo. If updating a nuclear submarine sounded impressive, patching an AI cluster on an oil tanker crossing the Java Sea sounded almost fictional. Palantir promised automatic rollback if memory consumption rose too much. It worked.
CIOs understood Apollo as insurance against the bureaucracy of change: fewer maintenance windows, fewer review boards, fewer surprises during peak production. Paying for calm without latency became acceptable, even elegant.
AI at the Door
In Chicago, a financial analyst typed a question: where does the cost of capital spike if oil rises to 105 dollars? The system responded with a Python script ready to run and a scenario chart seconds later. AIP, Palantir’s Artificial Intelligence Platform, had entered the office.
The trick was not only the language model, but the constraints around it. Every answer was wrapped in permissions. The internal lawyer could inspect which query touched which column before the chart was shown. Creativity, yes; data libertinism, no.
The Fine Print
As adoption grew, so did the contractual fine print: rights over future savings, embedded engineering access, and sometimes equity if a client built spin-offs from the platform. Start-ups welcomed the muscle. Large corporations saw the shadow of lock-in.
Auditors began speaking of a Palantir divorce rate: the years and millions required to migrate away. Once ontologies bind tables together, every row of business becomes tattooed with proprietary metadata.
Brussels Returns
European digital-resilience rules forced banks using Gotham or Foundry to prove that a vendor failure would not leave them blind. Palantir answered with an island mode that seals the system and allows operation without live cloud calls. Regulators asked less what does it do? and more what could it break if it fails?
Epilogue: Silicon Pulses and Office Coffee
At 3:17 a.m., inside a global retailer’s logistics center, an operator drinks warm coffee and watches heat maps shift. The algorithm reroutes more than two thousand trucks around a storm in Kansas while the country sleeps.
Nobody will call that operator a hero of e-commerce, but the quarterly margin may record the result. Palantir’s private-sector chapter is quiet, pragmatic, and sticky as machine oil. The old questions return wearing new clothes: who audits the oracle, and who decides whether a lost minute is worth exposing an entire value chain to an eye that never blinks?
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.
