The Farmer and the Algorithm

The Farmer and the AlgorithmHow artificial intelligence is quietly transforming the way we grow food — and why the most exciting opportunity has nothing to do with robots harvesting lettuce.Picture a farm in central Illinois. About 3,000 acres of corn and soybean country, worked by the same family for generations. A few years ago, the farmer there decided to try something different. He cut back on tillage, planted cover crops between his cash crop cycles, and started paying attention to his soil in a way he hadn’t before. Not just whether it was wet or dry, but what was living in it.The results surprised him. Moisture retention went up. The soil held more organic matter. Nutrients cycled better. His fields, over time, started to feel different underfoot — spongier, more alive. He described the transition as demanding, though. New knowledge, new equipment, and a period of uncertainty while biology got its footing.What he was practicing has a name:regenerative agriculture. And it is increasingly being paired with a technology that might seem, at first glance, completely alien to muddy boots and field notebooks: artificial intelligence.This is a story about that pairing; what it actually looks like, why it matters, and what it might mean for the future of food.Malone News is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.First, a word about scaleAI in agriculture is not a fringe experiment. It is a rapidly growing industry, projected to expand from $1.7 billion in 2023 to $4.7 billion by 2028.1Estimates suggest that combining AI with digital farming tools could add more than $450 billion a year to agricultural output in developing countries alone, a 28 percent improvement over what those countries might otherwise achieve.1The pressure driving this investment is real. Agriculture already accounts for 72 percent of all the freshwater humans withdraw from rivers, lakes, and aquifers.2About a third of the world’s farmland is degraded, so that it has become less productive.2And by 2050, we will need to feed roughly ten billion people. That is a lot of pressure on a system that is already showing cracks.AI is already involved in farming activities that increase production and reduce resource use. A major 2025 review of four years of field research found that AI-powered tools had moved well beyond lab demonstrations and into real farms, achieving accuracy rates above 90 percent in detecting crop diseases and pests, and delivering measurable improvements in how efficiently farms use water, fertilizer, and other resources.3What AI is actually doing on farms right nowIf you want to see where AI is already earning its keep, start with precision farming. The idea is simple: instead of treating an entire field as a uniform surface and applying the same water, fertilizer, and pest control everywhere, you treat each square meter individually based on real data. AI makes that possible by processing the flood of information coming from satellites, drones, soil sensors, and weather stations faster and more accurately than any human could.Computer vision systems can now identify the first signs of fungal disease in a crop before a farmer walking the field would notice anything wrong. Robotic weeders — like those built by Harvested Robotics — can roll through a field and mechanically pull weeds without herbicide, distinguishing weeds from seedlings by size and species.5Predictive platforms crunch historical weather patterns, soil data, and market prices to help farmers decide when to plant, when to irrigate, and when to harvest.According to one thorough review of the field, AI is transforming farming across the entire supply chain, not just what happens in the field, but how food is processed, stored, and distributed.4A compound annual growth rate of 25.5 percent over the next few years reflects how seriously the industry is taking this.4But here is the problem. Most of these tools were built to optimize conventional, industrial farming; to grow more corn per acre of flat, chemically managed land. Applied to that kind of farming, AI can actually make some existing problems worse: pushing monocultures harder, reducing ecological diversity, and treating the farm as a factory floor rather than a living system.6The more interesting question is what happens when you point these tools in a different direction.What regenerative farming actually isBefore we get to the AI-regenerative agriculture connection, it helps to be clear about what regenerative farming actually means, since the term gets used loosely.At its core, regenerative agriculture is the practice of farming in a way that actively improves the land rather than just extracting from it. That means using less tillage (or none at all, since plowing destroys soil structure and kills microbes). It means planting cover crops — things like clover, rye, or radishes — between cash crop cycles to keep living roots in the ground. It means bringing animals back into the rotation in managed ways, so their grazing and manure work with the land’s biology rather than against it. It means reducing synthetic fertilizers and pesticides and, as much as possible, replacing them with biological processes.The goal is a farm where the soil gets better every year: more organic matter, better water retention, richer microbial life, more resilience against drought and heavy rain. It is a fundamentally different objective from conventional farming, which often maintains soil as a medium for holding crops upright while chemicals do most of the work.What makes regenerative farming hard is that it iscomplex. Every decision about which cover crop to plant, when to graze, how to rotate, how much to till, interacts with dozens of local variables: soil type, rainfall, temperature, crop variety, and field history. There is no universal playbook. What works brilliantly in Iowa may fail in Georgia. This is precisely where AI becomes useful, because AI is very good at holding complexity.The numbers behind the shiftA 2024 McKinsey survey of farmers worldwide found that 68 percent had already adopted crop rotations, 56 percent had moved to reduced- or no-till farming, and 40 percent were using variable-rate application technology. This means they were already adjusting their inputs field by field rather than blanket-spraying.7These are not niche practices anymore. They are becoming mainstream.Researchers and practitioners working at the intersection of AI and regenerative farming have identified five areas where the combination pays off most clearly: planning regenerative landscapes at scale using satellite data, tailoring practices to local conditions, reducing the financial risk of transitioning away from conventional methods, creating accountability in supply chains so buyers can verify what farmers are actually doing, and providing continuous field-level monitoring.8All of these depend on the same underlying capability: making sense of large amounts of heterogeneous data quickly and translating it into decisions a farmer can act on tomorrow morning.The soil is the whole gameIf there is one thing that unites every aspect of regenerative agriculture, it is the soil. Everything from water retention, nutrient cycling, resilience to weather stress, and long-term productivity flows from having healthy, biologically rich, structurally intact soil. And for most of agricultural history, measuring soil health was slow, expensive, and inconsistent.The conventional approach involved physically extracting soil samples, sending them to a laboratory, waiting weeks for results, and receiving measurements for a handful of locations across a field that might vary enormously. It was a bit like trying to understand a patient’s health by testing blood from five spots on their body, once a year, with a month’s delay between sample and result.AI and satellite remote sensing are changing that completely. A 2025 study used data from two European Space Agency satellites, combined with a machine learning model called XGBoost, to map soil organic matter across farm sites in Japan and Togo. The model achieved a high degree of accuracy, which is exceptional for this kind of work, at a fraction of the cost of traditional sampling.9What took months and lab fees can now be done continuously, from orbit.A group of companies is developing commercial tools based on this science. Biome Makers sequences the DNA of soil microbial communities to provide farmers with a live portrait of their soil biology, showing which organisms are present, which are thriving, which are missing, and what that indicates for nutrient availability and plant health. Another company uses satellite imagery and machine learning to continuously monitor soil health indicators across entire farm portfolios, translating those measurements into outcome-based payments for farmers who show genuine improvement. Another soil modeling platform can reduce the cost of soil assessment by up to 90 percent compared to traditional sampling. And there is another methodology now officially approved by Verra (the global certification body) for evaluating soil health through digital mapping.10Proving it actually worksOne of the persistent frustrations in regenerative agriculture has been the difficulty of rigorously and cheaply proving that practices produce the outcomes farmers and buyers claim. A food company that wants to source from regenerative farms needs more than the farmer’s word for it. An investor financing the transition needs evidence. Historically, that evidence has been expensive to gather and inconsistent in quality.11Furthermore, organic food labelling has proven to be an unreliable method for ensuring quality.This is where AI-powered remote sensing becomes genuinely transformative. The same satellite platforms that can assess soil health can also monitor whether cover crops were actually planted, whether fields are showing signs of improved water retention, and whether biodiversity indicators are moving in the right direction. Continuous monitoring at low cost means that outcome-linked payments: paying farmers for verified ecological results rather than just for adopting certain practices, become financially feasible at scale.12Because this is an issue of scale.  If we want to replace glyphosate and petrochemical fertilizers in order to feed billions of people, regenerative farming has to be done at scale.The legal and regulatory scaffolding is starting to catch up. In the United States, the Growing Climate Solutions Act created a USDA-administered pathway for farmers to participate in outcome-based agricultural markets. The European Union’s Carbon Removals and Carbon Farming Regulation, passed in 2024, established a certification framework with clear standards for verifying ecological outcomes from farmland. These are the institutional structures that AI-powered verification is being built to serve.13The hardest part isn’t the technologyAsk anyone who has tried to transition a farm from conventional to regenerative practices, and they will tell you the hardest part is not buying new equipment or accessing new markets. It is the knowledge gap. Conventional farming is a well-documented, heavily supported system. There are extension agents, company representatives, and decades of research telling you exactly what to do in most situations. Regenerative farming is more experimental, more local, more dependent on the farmer’s own observation and judgment.That Illinois farmer with 3,000 acres, no-till, cover crops, and careful attention to soil biology described the transition as demanding precisely because so much of the knowledge had to be built from scratch, one season at a time. Things improved. Soil water capacity went up. Organic matter levels rose. Nitrogen and phosphorus availability improved.14But it took time, and there was real uncertainty along the way.AI-powered advisory tools are beginning to close this gap. In May 2025, Farmland LP, a company that manages regenerative farmland as an investment asset, partnered with Microsoft’s Digital Impact Studio to explore how AI could support its operations. They identified around 35 potential applications and focused on 15 with the clearest near-term value. The starting point was unglamorous but essential: pulling together data that was scattered across accounting systems: field records, maps, and handwritten notes into a single place where AI could actually work with it. From there, conversational AI assistants, predictive crop models, and real-time alerts made it possible for farm managers to make better decisions faster, without drowning in information.15The corporation, Boomitra, has technology that enables the measurement, reporting, and verification of soil carbon content, plant health, and soil moisture levels.Boomitra is taking a version of this approach to smallholder farmers in South Asia and sub-Saharan Africa. Their AI assistant communicates in local languages and helps farmers implement regenerative practices step by step, while the platform’s satellite monitoring tracks whether those practices are actually improving the land. Verified outcomes trigger payments. The whole system: advice, monitoring, compensation, is designed to be accessible to someone with a basic smartphone and no technical background.16Thinking bigger than a single fieldOne limitation of traditional farm management is that it stops at the fence line. But ecosystems do not. Water quality in a river depends on what is happening across its entire watershed. Pollinator populations depend on connected corridors of habitat. The resilience of a farming landscape, its ability to handle drought, flood, and pest pressure, depends on biodiversity and soil health distributed across many farms and landowners.AI is starting to enable management at this larger scale. Satellite-based AI models can analyze land cover, soil conditions, and water availability across entire regions, identifying where regenerative practices would have the greatest impact and helping coordinate action across multiple landowners. Pilot programs in India’s Madhya Pradesh state have used this approach, integrating geospatial data from weather and satellite services to guide landscape-level planning and link that planning to financing for participating farmers.17In Colombia’s Boyaca region, a similar initiative built around regenerative practices and digital tools produced a 36 percent increase in barley productivity, through better knowledge and coordination.17A related trend is what the agricultural industry is calling “nature positive” farming. This is an approach that goes beyond soil health to actively measure gains in biodiversity. Acoustic monitoring technology can track which bird and insect species are present on a farm over time. Image recognition tools can survey plant diversity. These measurements are becoming part of the ecological scorecard that buyers, investors, and regulators are starting to ask for.18The virtual farmOne of the more futuristic applications on the horizon is what researchers call an agricultural digital twin: a real-time virtual model of a farm that mirrors what is actually happening in the field. The idea is that a farmer could test a new cover crop rotation, a different grazing schedule, or a soil amendment strategy virtually before committing to it in the real world, learning from simulated outcomes rather than expensive trial and error. Given how nonlinear and complex regenerative systems are. how many variables interact in ways that are hard to predict. This kind of simulation could be enormously valuable.19The technology is still early, but it is one of the most-watched trends in agricultural technology for the next several years.What could go wrongIt would be dishonest to write about this technology without acknowledging its real risks. Three stand out.The first is data ownership. Regenerative farming is deeply local; what works on one farm depends on the specific biology, hydrology, and history of that piece of land. Useful AI models need dense, high-quality local data, and that data is generated by farmers. If the commercial value from aggregating all that farm data flows primarily to technology companies and investors rather than to the farmers who created it, the technology will reproduce and deepen existing inequalities rather than addressing them.19The second is bias. AI models are only as good as the data they were trained on. If the training data comes predominantly from large, well-capitalized farms in North America and Western Europe, the models will not perform well for smallholder farmers in Asia, Africa, and Latin America, who grow a large proportion of the world’s food. There is a real risk that AI-powered regenerative agriculture becomes an expensive tool for wealthy farms in wealthy countries, while the farmers who most need support are left behind.20The third is cost. Drones, sensors, satellite subscriptions, AI advisory platforms, these things add up. A farmer who is already absorbing the income uncertainty of transitioning away from well-understood conventional practices is not well-positioned to also absorb significant technology costs. Making these tools widely accessible requires thoughtful subsidy programs, cooperative models, and public investment in the underlying data infrastructure.Craig Wichner, who founded Farmland LP, put it well when he wrote that farming is deeply human work — that it requires judgment, intuition, and a relationship with the land that no algorithm can replicate. The most useful framing of AI’s role, he suggested, is not as a replacement for farmers but as a way of handling the tedious parts — data entry, monitoring alerts, routine analysis, so that farmers can spend more time on the work that actually requires their presence and expertise.21What this is really aboutHere is the thing about AI in agriculture that tends to get lost amid coverage of robots, satellites, and machine learning models: the technology itself is not the point. Technology is a tool. What matters is what you use it for.For most of AI’s history in farming, it has been pointed at a narrow set of targets: yield per acre, cost per bushel, output per input. Those are important goals, but they are not the only ones that matter. A farm that maximizes short-term yield while depleting its soil, draining its aquifer, and eliminating the biodiversity that keeps it resilient is not a successful farm; it is a slow-motion failure.What regenerative agriculture asks is that we optimize for different things: soil health, water retention, ecological function, and long-term productivity. These outcomes are harder to measure and slower to show up in quarterly reports. But they are the outcomes on which sustainable food production actually depends.AI, properly directed, can make measuring and optimizing for those outcomes as tractable as measuring and optimizing for yield. The satellite platforms, the soil microbiome analyzers, the AI advisors, the outcome verification systems described in this piece are not theoretical. Many of them are running on farms today. The question is not whether the technology works. The question is whether we will build the supporting structures: equitable data governance, accessible financing, reliable verification standards, smart policy, that allow it to reach its potential.That Illinois farmer is watching his soil get better, season by season, with better tools than he had when he started. That is what this technology, at its best, is for.Thanks for reading Malone News! This post is public so feel free to share it.ShareNotes1. AI market projection from MarketsandMarkets, “AI in Agriculture Market Size, Share & Trends Analysis Report,” 2024; digital agriculture GDP estimate from“Farms of the Future: How Can AI Accelerate Regenerative Agriculture?”, accessed February 2026.2. Food and Agriculture Organization of the United Nations, “The State of the World’s Land and Water Resources,” FAO, 2022,https://www.fao.org/land-water/en/.3. Adinarayana et al., “Artificial Intelligence in Sustainable Agriculture: Towards a Socio-Technical Roadmap,” ScienceDirect, October 27, 2025,https://www.sciencedirect.com/science/article/pii/S2772375525008093.4. Ajaharuddin et al., “Artificial Intelligence in Agriculture: Ethics, Impact Possibilities, and Pathways for Policy,” ScienceDirect, September 2, 2025,https://www.sciencedirect.com/science/article/pii/S0168169925010336.5. StartUs Insights, “10 AI Solutions for Agriculture to Watch in 2025,” June 6, 2025,https://www.startus-insights.com/innovators-guide/ai-solutions-for-agriculture/.6. Omdena, “AI for Regenerative Agriculture,” December 18, 2025,https://www.omdena.com/blog/ai-agriculture-regenerative-practices-us.7. David Fiocco et al., McKinsey Global Farmer Insights 2024 (McKinsey and Company, 2024).8. Adinarayana et al., “Artificial Intelligence in Sustainable Agriculture,” ScienceDirect, October 27, 2025,https://www.sciencedirect.com/science/article/pii/S2772375525008093.9. Adinarayana et al., “Remote Sensing-Based Soil Organic Carbon Monitoring Using Advanced Machine Learning Techniques,” ScienceDirect, May 21, 2025,https://www.sciencedirect.com/science/article/pii/S2772375525002692.10. Biome Makers corporate overview,https://biomemakers.com; Boomitra, “Inside Boomitra’s Proven Technology,” September 17, 2025,https://boomitra.com/inside-boomitras-proven-technology/; EOS Data Analytics, “How EOSDA Uses AI and ML to Monitor Soil Health Indicators,” May 10, 2024,https://eos.com/blog/how-eosda-monitors-sequestrated-carbon-with-ai-and-ml/; Perennial Earth,https://www.perennial.earth/.11. GlobeNewswire, “Agriculture Soil Health Market Research 2024-2034,” January 14, 2025,https://www.globenewswire.com/news-release/2025/01/14/3009211/28124/en/Agriculture-Carbon-Sequestration-Market-Research-2024-2034.12. Tech4Future, “AI Techniques for Soil Fertility Monitoring in Agricultural Systems,” July 26, 2024,https://tech4future.info/en/carbon-farming-ai-co2-agricultural-soil/.13. United States Department of Agriculture, “Growing Climate Solutions Act: Implementation and Farmer Participation,” USDA.gov, 2023,https://www.usda.gov.14. Craig Wichner, “Farming Smarter: How AI Can Help Farmers Regenerate Land, Empower Workers and Feed the Future,” Green Money, October 5, 2025,https://greenmoney.com/new_version/farming-smarter-how-ai-can-help-farmers-regenerate-land-empower-workers-and-feed-the-future/.15. Wichner, “Farming Smarter,” Green Money, October 5, 2025.16. Boomitra, “Inside Boomitra’s Proven Technology,” September 17, 2025,https://boomitra.com/inside-boomitras-proven-technology/.17. Agmatix, “Regenerative Agriculture Outcomes: Field Evidence from Colombia and India,” Agmatix Research Notes, 2025,https://www.agmatix.com/blog/top-5-agtech-trends-for-2025-whats-next-for-regenerative-agriculture/.18. Agmatix, “Top 5 AgTech Trends for 2025,” accessed February 2026,https://www.agmatix.com/blog/top-5-agtech-trends-for-2025-whats-next-for-regenerative-agriculture/.19. Adinarayana et al., “Artificial Intelligence in Sustainable Agriculture,” ScienceDirect, October 27, 2025,https://www.sciencedirect.com/science/article/pii/S2772375525008093.20. Ajaharuddin et al., “Artificial Intelligence in Agriculture: Ethics, Impact Possibilities, and Pathways for Policy,” ScienceDirect, September 2, 2025,https://www.sciencedirect.com/science/article/pii/S0168169925010336.21. Wichner, “Farming Smarter,” Green Money, October 5, 2025,https://greenmoney.com/new_version/farming-smarter-how-ai-can-help-farmers-regenerate-land-empower-workers-and-feed-the-future/.Bibliography1. Adinarayana et al. “Artificial Intelligence in Sustainable Agriculture: Towards a Socio-Technical Roadmap.” ScienceDirect, October 27, 2025.https://www.sciencedirect.com/science/article/pii/S27723755250080932. Adinarayana et al. “Remote Sensing-Based Soil Organic Carbon Monitoring Using Advanced Machine Learning Techniques.” ScienceDirect, May 21, 2025.https://www.sciencedirect.com/science/article/pii/S27723755250026923. Agmatix. “Regenerative Agriculture Outcomes: Field Evidence from Colombia and India.” Agmatix Research Notes, 2025.https://www.agmatix.com/blog/top-5-agtech-trends-for-2025-whats-next-for-regenerative-agriculture/4. Ajaharuddin et al. “Artificial Intelligence in Agriculture: Ethics, Impact Possibilities, and Pathways for Policy.” ScienceDirect, September 2, 2025.https://www.sciencedirect.com/science/article/pii/S01681699250103365. Boomitra. “Inside Boomitra’s Proven Technology.” Boomitra.com, September 17, 2025.https://boomitra.com/inside-boomitras-proven-technology/6. EOS Data Analytics. “How EOSDA Uses AI and ML to Monitor Soil Health Indicators.” EOS.com, May 10, 2024.https://eos.com/blog/how-eosda-monitors-sequestrated-carbon-with-ai-and-ml/7. Fiocco, David, Vasanth Ganesan, Ana Luiza Mokodsi, Franziska Alesso, and Otto Gryschek. McKinsey Global Farmer Insights 2024. McKinsey and Company, 2024.8. Food and Agriculture Organization. “The State of the World’s Land and Water Resources.” FAO, 2022.https://www.fao.org/land-water/en/9. GlobeNewswire. “Agriculture Soil Health Market Research 2024-2034.” January 14, 2025.https://www.globenewswire.com/news-release/2025/01/14/3009211/28124/en/Agriculture-Carbon-Sequestration-Market-Research-2024-203410. Lal, R. “Artificial Intelligence and Soil Modeling Demystified: Power, Potentials, and Perils.” Carbon Footprints 1 (2022): 1-12.https://www.oaepublish.com/articles/cf.2022.0311. Omdena. “AI for Regenerative Agriculture.” Omdena.com, December 18, 2025.https://www.omdena.com/blog/ai-agriculture-regenerative-practices-us12. Perennial Earth. Accessed February 2026.https://www.perennial.earth/13. StartUs Insights. “10 AI Solutions for Agriculture to Watch in 2025.” June 6, 2025.https://www.startus-insights.com/innovators-guide/ai-solutions-for-agriculture/14. Tech4Future. “AI Techniques for Soil Fertility Monitoring in Agricultural Systems.” July 26, 2024.https://tech4future.info/en/carbon-farming-ai-co2-agricultural-soil/15. United States Department of Agriculture. “Growing Climate Solutions Act: Implementation and Farmer Participation.” USDA.gov, 2023.https://www.usda.gov16. Wichner, Craig. “Farming Smarter: How AI Can Help Farmers Regenerate Land, Empower Workers and Feed the Future.” Green Money, October 5, 2025.https://greenmoney.com/new_version/farming-smarter-how-ai-can-help-farmers-regenerate-land-empower-workers-and-feed-the-future/


Sunday Strip: Now What?

“Were gonna be able to vacation in Gaza, Cuba, Venezuela, Iran, and maybe Minnesota soon. Incredible times.”Malone News is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Thanks for reading Malone News! This post is public so feel free to share it.ShareJGM

Summary: This is just the beginning.


Book Review- 3/11: Viral Takeover

Sonia Elijah’s3/11: Viral TakeoverAvailable at Amazon for Pre-Orderat this linkThe print (paperback and hardback) and ebook will all become available on March 11In Brief3/11 Viral Takeoverby Sonia Elijah is a meticulously researched investigative account that pulls back the curtain on the events set in motion when the World Health Organization declared a global pandemic on March 11, 2020. Drawing on public records, FOI responses, leaked documents, and scientific literature, Elijah takes readers on a rigorous journey through the contested origins of COVID-19, the unprecedented policy decisions that reshaped everyday life, and the powerful institutions — from unelected scientific bodies to pharma-linked philanthropies — that shaped the global response. From the flawed PCR protocols and billion-dollar procurement scandals to the suppression of early treatments and the rushed authorization of mRNA vaccines, this book asks the hard questions that mainstream discourse too often sidestepped, making it essential reading for anyone committed to understanding one of the most consequential episodes in modern history.OverviewSonia Elijah’s3/11: Viral Takeoveris one of the most comprehensive, courageously investigative, and unflinchingly moral works to emerge from the post-pandemic reckoning. Drawing upon FOIA documents, leaked emails, and exclusive interviews with scientists, whistleblowers, and policy insiders, Elijah reconstructs how a network of health officials, philanthropic billionaires, and intelligence-linked research foundations conspired—deliberately or by convenience—to manipulate the global COVID-19 narrative. March 11, 2020, when the WHO declared a global pandemic, was not the start of a health emergency but acivilizational pivot—from democratic governance to biosecurity management. Elijah treats this date as “3/11,” the biological sequel to “9/11.” Where the earlier event birthed mass surveillance and endless war, 3/11 ushered in the age of obedience, censorship, and algorithmic control.The book is both an archival excavation and an indictment. Elijah exposes how narratives once labeled “conspiracy” later proved grounded in suppressed evidence. She traces the patterns—fear as leverage, science as cover story, data as propaganda—and argues that 3/11 was less a public health failure than acalculated information operationto re-engineer societal structures under the guise of pandemic response.Chapter-by-Chapter AnalysisPreface and IntroductionThe book begins viscerally: Elijah recalls seeing families walking through taped grocery aisles during lockdown, fearful of their own footsteps. From that moment she realized:this was not about a virus; it was about control.The introduction situates 3/11 as the symbolic day global democracies imported authoritarian containment methods pioneered by the Chinese Communist Party. Drawing a parallel with 9/11, she argues that while that day militarized the world externally, 3/11 internalized the war—turning populations into the monitored subjects of a continuous biopolitical experiment.Chapter 1: Locking in the NarrativeThis foundational chapter uncovers how a small group of interconnected scientists and funders locked the world into the “natural origin” story. Through forensic tracking of theLancetletter,Proximal Origin of SARS-CoV-2inNature Medicine, andThe Origins of SARS-CoV-2inCell, Elijah exposes coordination among Anthony Fauci, Jeremy Farrar, Peter Daszak, and others to dismiss the lab-leak hypothesis before facts were investigated. She documents how Farrar microedited critical language—changing “unlikely” to “improbable”—to fortify the illusion of consensus. The chapter’s revelation is stark:science was scripted, not discovered.Chapter 2: Cracks in the NarrativeElijah introduces the suppressedPradhan et al.preprint identifying four HIV-like inserts and the now-famed furin cleavage site. Within days it was withdrawn. Citing internal NIH correspondence, she shows that leading officials privately conceded the genome “looked engineered.” Fauci’s own midnight emails floated the possibility of MI5 or FBI involvement. Nobel virologist Luc Montagnier’s later confirmation of these inserts is revisited as proof of suppression. This chapter demonstrates howearly scientific dissent was extinguished before public scrutiny could even begin.Chapter 3: “Expert” TakeoverHere, Elijah widens the lens to show how unelected elites—officials at the WHO, Wellcome Trust, Gates Foundation, and American biodefense agencies—monopolized pandemic advice. She traces funding pipelines connecting these philanthropies to state research budgets and major pharmaceutical firms, exposing a seamless circle of influence. Behavioral psychologists were embedded into national policy teams to “nudge” populations into submission. Science gave cover;psychology delivered obedience.Chapter 4: Lockdowns in LockstepUsing official documents and testimony, Elijah dismantles the myth that lockdowns were scientifically necessary. She follows the thread from Neil Ferguson’s wildly inaccurate Imperial College models to the coordinated propagation of identical policies across continents. The title “Lockstep” nods to the 2010 Rockefeller Foundation scenario predicting global control through pandemic response. Elijah concludes these measures were rehearsals for governing through fear.Chapter 5: Locking Down HarmsThis emotional section humanizes the collateral damage. Elijah confronts the suicides, domestic abuse, learning loss, untreated illnesses, and silent deaths in care homes. Through government data and personal accounts, she argues lockdowns created “ethical blackouts”—policies so catastrophic yet normalized that bureaucrats stopped recognizing moral responsibility altogether.Chapter 6: The Flawed “Gold Standard”A forensic exposé of PCR testing. Elijah demonstrates how Christian Drosten’s diagnostic protocol—approved by the WHO within 24 hours—was never validated for accurate diagnosis. She argues PCR magnified statistical noise into pandemic panic, converting false positives into the currency of fear. This chapter reframes COVID testing asan industrial revenue machine masquerading as science.Chapters 7 & 8: The Innova ScandalThese middle chapters read like investigative thrillers. Elijah describes how the California firm Innova secured multibillion-dollar UK contracts for Chinese-made rapid tests later declared unusable by the FDA. She documents the political favoritism, VIP fast-tracking, and staggering waste of public funds. What emerges is a microcosm of global pandemic profiteering—a feeding frenzy justified by fear.Chapter 9: The Long ShotElijah traces the origins of mRNA technology, from DARPA’s “Pandemic Prevention Platform” to Operation Warp Speed. She reveals that long before the crisis, American defense agencies and biotech firms saw mRNA as the next frontier of dual-use technology—promising both defense and profit. Regulatory shortcuts, flawed toxicity studies, and political pressure unfolded at breathless speed. The “warp” referred not to scientific genius but to bureaucratic collapse.Chapter 10: Eliminate the CompetitionThis chapter exposes how old, inexpensive therapeutics like ivermectin and hydroxychloroquine were systematically neutralized. Elijah presents communications between regulators, NGOs, and social media giants showing coordinated censorship of dissenting physicians. These drugs were sacrificed on the altar of monopoly.Chapter 11: Rush to MarketDrawing on leaked European Medicines Agency emails, Elijah depicts panic within regulatory bodies over unstable formulations and contamination risks of early mRNA batches. Yet approval proceeded, driven by geopolitical optics—governments needed the illusion of triumph. This chapter portraysthe fusion of political theater and pharmaceutical commerce.Chapter 12: Beyond the LabelElijah expands on widespread contamination of vaccine lots and discrepancies between clinical and commercial batches. She argues regulators knowingly accepted inconsistent-quality biologics under emergency authorization, concealed from the public through redacted reports.Chapter 13: Faulty modRNA Tech and Disputed Data IntegrityHere, Elijah critiques the 2023 Nobel-winning mRNA technology as “faulty” for causing ribosomal frameshifting that produces aberrant proteins and unintended immune responses. She details the “Blotgate” scandal, including questionable and potentially duplicated Western blot data submitted by BioNTech/Pfizer to regulators that failed to confirm proper spike protein expression.Chapter 14: The Blind WatchdogsElijah presents internal government reports proving regulators were aware of severe adverse effects—thrombosis, myocarditis, menstrual changes—before official acknowledgment. Yet rather than sounding alarms, they issued promotional assurances. The watchdogs, she writes, “guarded the narrative instead of the people.”Chapter 15: Censoring HarmsA synthesis of media analysis and firsthand testimony, this chapter catalogues the global censorship machinery. Elijah traces networks like the Trusted News Initiative, revealing how governments, platforms, and legacy media jointly policed speech to preserve the illusion of consensus. Injured vaccine recipients were erased from public discourse; independent scientists were digitally exiled.Chapter 16: “For Your Safety”The closing argument extends beyond COVID to show how temporary emergency powers are being codified into permanent infrastructure—Digital ID systems, the WHO’s Pandemic Accord, and the convergence of health surveillance with financial and biometric tracking. Elijah’s final warning:biosecurity has replaced democracy as the organizing principle of Western governance.Overall ImpressionElijah’s journalistic method combinesarchival precisionwithinvestigative audacity. Each chapter builds an overwhelming evidentiary mosaic: scientists deceiving for funding protection; regulators abdicating; media acting as censors; philanthropies functioning as shadow governments. What might have been dismissed as speculative is here anchored in publicly verifiable documents.Unlike traditional pandemic retrospectives, Elijah refuses detachment. The prose burns with moral clarity. She does not posture as neutral—she prosecutes. Her argument is not merely that mistakes were made, but that the architecture of global health is structurally corrupt, rewarding deception and punishing transparency.Stylistically, the book fuses the pacing of a political thriller with the sobriety of a legal brief. The persistent theme is inversion: those who claimed to “follow the science” were actuallyfollowing the funding.Science did not guide power; power dictated science.Conclusion3/11: Viral Takeoveris the essential document for understanding how modern governance operates through crisis. It transforms chaotic news fragments into a coherent history of systemic manipulation. Elijah’s thesis is unsettling but liberating: the pandemic was astress test on human freedom, and we failed it. Yet failure need not be final—truth can still be reclaimed if citizens demand transparency and reject managed fear.Where official histories will downplay, sanitize, or forget, Elijah’s work preserves the record with forensic intensity. It is botha requiem for lost liberty and a manual for resistance.In an era when “disinformation” is defined by those committing it, this book reclaims the moral high ground for investigative truth.To read3/11: Viral Takeoveris to understand that the greatest pandemic was not viral—it was the infection of trust itself.About the authorSonia Elijah is an independent investigative journalist and former BBC researcher with a background in economics. Renowned for her meticulous, forensic-style reporting, she has conducted in-depth, evidence-based investigations into the COVID-19 response, including detailed analyses of Pfizer-BioNTech clinical trial documents, vaccine safety concerns, excess deaths, regulatory failures, and institutional conflicts of interest. Her work has been published by the Brownstone Institute, Trial Site News, The Conservative Woman, and other independent outlets. Through her widely read Substack Sonia Elijah Investigates and her podcast, she continues to deliver hard-hitting journalism that challenges mainstream narratives. 3/11 Viral Takeover is her debut book.Thanks for reading Malone News! This post is public so feel free to share it.ShareMalone News is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Summary: A brave new book about the COVIDcrisis by British Journalist and Author Sonia Elijah


Friday Funnies: "I Speak Jive"

Finding good Newsom jokes today is like shooting fish in a barrel!OK- for those that missed the actual video, here it is! Comedy doesn’t get much better!The World’s a Stage and Newsom is Front and CenterHe doesn’t govern so much as he tours. Every microphone becomes a spotlight, every press appearance a carefully staged scene, while the state he’s supposed to manage simmers on the back burner. Wildfires flare, water systems creak, rolling blackouts hum — but those aren’t cues for him, just background noise to the next photo op. The problem with living under the lights is that they don’t just illuminate, they expose. When you build a career on visibility, every crack shows in high definition. The real work waits in the wings while the roadshow rolls on, polished optics and soundbites front and center. In a world where attention is currency, overexposure is the cost.He stands in the limelight while backstage, the theater is ablaze.from: Worlds AmissMalone News is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Thanks for reading Malone News! This post is public so feel free to share it.Share

Summary: Exit Stage left - Gruesome Newsom


How to Train Your AI

How to Train Your AIA Plain-English Guide to Building, Teaching, and Safeguarding Artificial IntelligenceArtificial intelligence is no longer the stuff of science fiction. It answers our questions, writes our emails, and holds conversations that feel startlingly human. But how does it actually work? How is an AI built, taught, and kept from going off the rails? The answer is more fascinating, and more human, than most people realize.Part One: Building the BrainEvery AI starts with a goal. Do you want it to recognize faces? Translate languages? Answer questions? That goal determines everything that follows. Once the goal is clear, the real work begins, and the first ingredient needed is data. Enormous amounts of it.For a Large Language Model, which is the kind of AI behind chatbots and writing assistants, that data is text. Trillions of words drawn from books, websites, academic papers, and more. The goal is to expose the model to as much of human language and knowledge as possible, because AI learns from examples the same way humans do: through exposure and repetition.At the heart of the AI is something called a neural network, a mathematical structure loosely inspired by the human brain, made up of layers of connected nodes that pass information to one another. The network’s behavior is determined by billions of tiny numerical values called “weights,” which represent the strength of connections between those nodes. Training the AI is essentially the process of finding the right weights.Training works through a beautifully simple idea: prediction. The model is shown a sentence with the last word removed, and it tries to guess what that word is. It gets scored on how wrong it was. Then a process called backpropagation figures out which weights made the prediction worse and adjusts them slightly. Do this billions of times across trillions of words, and something remarkable happens: the model does not just learn grammar. It absorbs facts, reasoning patterns, and context. It begins to understand language, or something that functions very much like understanding.This phase, called pre-training, is staggeringly expensive. It requires thousands of specialized computer chips running for weeks or months, consuming vast amounts of electricity. The result is a “base model” that is extraordinarily good at generating fluent text, but also unpredictable and sometimes problematic. It has learned from all of human writing, which includes the full spectrum of human expression: the inspiring and the offensive, the truthful and the false.Part Two: Teaching It to BehaveA raw, pre-trained model is a bit like someone who has read everything ever written but has never been taught manners, ethics, or professional conduct. The next phase of development is about instilling those qualities, and it involves several overlapping techniques.Fine-TuningAfter pre-training, the model is trained again, this time on a much smaller, carefully curated set of high-quality conversations and responses. This teaches it to behave like a helpful, professional assistant rather than a raw text predictor. The model’s weights shift gradually toward producing the kinds of responses a thoughtful person would give.Reinforcement Learning from Human Feedback (RLHF)One of the most powerful techniques used today is called Reinforcement Learning from Human Feedback, or RLHF. The AI generates several different responses to the same prompt, and human reviewers rank them from best to worst. A separate “reward model” is trained to predict what humans prefer. Then the main AI is trained to maximize that reward, essentially learning to produce responses that real people find helpful, accurate, and appropriate.Through this process, guardrails, or more formally safety mitigations and alignment measures, get woven directly into the model’s weights. It is not that a rulebook gets programmed in. It is that the model’s deeply ingrained tendencies are shaped, through thousands of examples and feedback cycles, to steer away from harmful outputs. Think of the difference between giving a child a printed list of rules versus raising them with consistent guidance, feedback, and example. The AI’s values, such as they are, develop through the latter approach.Constitutional AISome companies go a step further, training the AI to critique its own responses against a set of core principles that function essentially as a constitution for the model’s behavior. The AI learns to ask itself whether a response is honest and whether it could cause harm, then revise accordingly before settling on a final answer.System Prompts and Hard FiltersLayered on top of the trained behavior are more traditional software tools. System prompts are invisible sets of instructions given to the AI before each conversation begins, telling it how to behave in a specific context. Hard filters are conventional code sitting outside the model that scan inputs and outputs for prohibited content and block them before they reach the user. These act like a bouncer at the door, while the trained behavior acts like the internalized conscience of the person inside.System prompts can even include tiered access, essentially passwords or keys that allow different users to unlock different levels of AI capability. An administrator with the right key might access features unavailable to a general user. However, this approach has real limitations: because the AI processes system prompts and user messages through the same mechanism, a clever user may be able to extract or circumvent them. For high-stakes applications, true security is better handled by the surrounding software rather than by trusting the AI to enforce it.Part Three: Testing in the SandboxBefore any AI is released to the public, it goes through a critical phase of testing in what is called a sandbox, which is a controlled, isolated environment where the model can be probed and stressed without any risk to real users or real systems. Think of it as a flight simulator for AI: trainee pilots can crash the plane a hundred times without anyone getting hurt.In the sandbox, engineers can safely test dangerous scenarios, observe unfiltered behavior, and experiment with new safety measures before deploying them. The AI might be cut off from the internet or sensitive systems, so even if it misbehaves, the damage is fully contained. When AI is given tools such as the ability to browse the web, run code, or interact with other software, those capabilities are sandboxed first to understand what could go wrong.A key part of sandbox testing is something called red-teaming. Researchers, sometimes humans and sometimes other AI systems, try their hardest to make the model misbehave: to get it to say something harmful, reveal restricted information, or bypass its guidelines through clever phrasing, roleplay scenarios, or encoding tricks. This is ethical hacking for AI. The vulnerabilities discovered through red-teaming are patched before the model goes live.Part Four: The Ongoing Challenge of JailbreakingOne of the most sobering truths about AI safety is that it is never finished. Because guardrails are embedded in the model’s weights rather than in explicit, readable code, they cannot be mathematically verified the way traditional software can. You cannot read the weights and confirm they are safe. You have to probe the model through testing and observe how it behaves.This creates what the industry calls a jailbreaking problem. Users who are determined to get an AI to misbehave can sometimes succeed by finding gaps in its training, asking questions in roundabout ways, using fictional framing, switching languages, or employing other creative techniques to make the model’s safety instincts fail to activate. It is an ongoing arms race: researchers find exploits, developers patch them, and new exploits emerge.There is also a fundamental tension that every AI developer grapples with: guardrails that are too tight make the AI useless, refusing to discuss anything remotely sensitive even for entirely legitimate reasons. Guardrails that are too loose allow harm. Finding and maintaining the right balance requires constant human judgment, ongoing monitoring of real-world conversations, and regular retraining as new problems are discovered.Part Five: The Hallucination ProblemOf all the challenges in AI development, hallucinations may be the most insidious. Unlike a jailbreak, where a bad actor has to work deliberately to extract harmful content, hallucinations happen on their own, uninvited, in the middle of otherwise helpful conversations. And they do so with complete confidence.An AI hallucination is when the model confidently states something that is factually wrong, inventing people, citations, events, statistics, or details that simply do not exist. The term is apt: the AI is not lying intentionally. It is generating text that sounds plausible based on patterns in its training data, even when no factual basis exists. It is the dark side of the same fluency that makes these models so impressive.The root cause goes back to how LLMs work. They are trained to predict the most statistically likely next word. They do not know facts the way a database does; they have learned patterns associated with facts. When asked something outside their confident knowledge, they do not naturally say they do not know. They do what they were trained to do: generate plausible-sounding text. The result can be a well-written, confidently delivered, completely fabricated answer.Retrieval-Augmented Generation (RAG)One of the most effective practical solutions is called Retrieval-Augmented Generation, or RAG. Rather than relying solely on what the model memorized during training, RAG connects the AI to an external knowledge source, such as a database, a document library, or the internet, at the moment a question is asked. The model retrieves relevant, current, verified information first, then generates its answer based on that retrieved content rather than pure memory. Think of the difference between answering a question from memory versus being allowed to look it up first. RAG dramatically reduces hallucinations on factual questions because the model is working from real source material it can reference.Teaching the Model to Say It Does Not KnowOne of the most powerful behavioral interventions is teaching the model to express uncertainty. Through fine-tuning and RLHF, models can be specifically rewarded for acknowledging when they are not certain and penalized for confidently stating things that turn out to be wrong. This does not prevent the model from being wrong, but it stops it from being wrong with confidence, which is arguably the more dangerous form of hallucination. A hedged wrong answer invites the user to verify. A confident wrong answer does not.Chain-of-Thought ReasoningInstead of jumping straight to an answer, models can be trained or prompted to reason step by step, showing their work so to speak. This approach, called chain-of-thought reasoning, tends to reduce hallucinations because each reasoning step can catch errors in the previous one. It also makes the model’s thinking visible, so users can spot where the logic went wrong rather than simply receiving a confident wrong conclusion.Grounding, Citations, and Fact-Checking LayersModels can be designed to cite their sources, pointing to specific documents or passages that support their claims. This forces the model to anchor its answers in retrievable evidence rather than relying on statistical intuition alone. If it cannot cite a source, it should say so. Many enterprise AI systems build this in as a hard requirement.Some systems go further, adding a second AI on top of the first, one whose sole job is to verify the claims made in the first model’s response against a trusted knowledge base. If a claim cannot be verified, it gets flagged or removed. A related technique called self-consistency checking has the model generate multiple independent answers to the same question and compare them. If all versions agree, confidence is higher. If they contradict each other, the model flags uncertainty. Hallucinations tend to be inconsistent across attempts, while true knowledge tends to be stable.Specialized Models and Controlled CreativityCounterintuitively, trying to make a model know everything can increase hallucinations. A model trained specifically on medical literature, for example, hallucinates far less on medical questions than a general-purpose model trying to cover all of human knowledge. Specialized models have a narrower but more reliable knowledge base.There is also a technical setting inside the model called “temperature” that controls how creative or random its outputs are. High temperature produces more varied, imaginative responses, but also more hallucinations. Lower temperature makes the model more conservative, sticking closer to patterns it has seen before. For factual applications, dialing down the temperature reduces the risk of the model wandering into invented territory.The Human in the LoopFor high-stakes applications in medicine, law, and finance, the most reliable safeguard remains a human expert reviewing the AI’s output before it is acted upon. AI handles the heavy lifting; a human catches the errors. No current technique eliminates hallucinations entirely. They are, to some extent, a fundamental consequence of how LLMs work. The goal of current research is not perfection; it is making hallucinations rarer, less confident, more detectable, and less consequential.Part Six: Can a Large Language Model Think?This is one of the most debated questions in all of artificial intelligence, and depending on who you ask, the answer ranges from an emphatic yes to an equally emphatic no. Can a Large Language Model actually think? The honest answer is that it depends entirely on what you mean by the word.On the surface, the case against thinking seems straightforward. An LLM does not reason the way a human does. It has no experiences, no curiosity, no inner life. It does not sit quietly and ponder a problem. What it does, at a mechanical level, is predict the next most likely word based on patterns absorbed from vast amounts of human text. It is, in that sense, an extraordinarily sophisticated pattern-matching engine. Critics who hold this view often say that LLMs do not think at all; they merely simulate thinking with enough skill to be convincing.But that view, while valid, leaves some important things unexplained. When an LLM solves a novel logic puzzle it has never encountered before, is it just matching patterns? When it catches an error in a legal argument, translates irony between languages, or generates a metaphor that genuinely illuminates an idea, what exactly is happening? The outputs sometimes go well beyond what simple pattern retrieval would predict. Something is being processed, recombined, and applied in ways that at least resemble reasoning.What the Research SuggestsResearchers have found that large language models, particularly those trained at scale, develop internal representations of concepts, relationships, and even something resembling logical structure. They can perform multi-step reasoning, draw inferences, and generalize from principles to new situations. These are behaviors that, in humans, we would not hesitate to call thinking.At the same time, LLMs fail in ways that human thinkers rarely do. They can be confidently wrong about simple arithmetic. They can contradict themselves within the same conversation. They can be fooled by rephrasing a question slightly differently, even when the underlying logic remains identical. These failures suggest that whatever is happening inside the model is not the same as human reasoning, even when the outputs look similar.The Chinese Room ProblemThe philosopher John Searle famously illustrated this tension with a thought experiment called the Chinese Room. Imagine a person locked in a room with a large rulebook for responding to Chinese characters. Messages in Chinese are passed under the door; the person looks up the appropriate responses in the rulebook and passes them back out; to anyone on the outside, the exchange looks like a fluent conversation with a Chinese speaker. But the person inside understands nothing. They are just following the rules.Searle argued that LLMs are essentially that person in the room: producing outputs that appear to reflect understanding without any actual comprehension behind them. The counterargument, made by many AI researchers, is that the human brain itself might be described as a very complex version of the same process, and that understanding may simply be what sophisticated information processing looks like from the inside.Neither side has definitively won that argument. It remains one of the genuinely open questions at the intersection of philosophy, neuroscience, and computer science.A More Useful Way to Frame the QuestionRather than asking whether LLMs can think, it may be more useful to ask what kinds of thinking they can do and what kinds they cannot. They are remarkably capable at synthesizing information, identifying patterns, generating creative connections, and producing well-structured arguments. They are considerably weaker at sustained logical chains that require holding many variables in precise relationship, at grounding their knowledge in real-world experience, and at knowing the limits of their own knowledge.In practical terms, LLMs think differently from humans, rather than not at all. They process language with a kind of breadth and fluency that no human could match, drawing on connections across billions of words. But they lack the embodied experience, the emotional grounding, and the genuine self-awareness that shape human thought in ways that go far beyond language.Perhaps the most honest answer is this: a Large Language Model does something that is genuinely impressive, genuinely useful, and genuinely worth taking seriously. Whether it rises to the level of thinking in the fullest sense of that word is a question that says as much about how we define thinking as it does about what the model is actually doing. And that question, for now, remains beautifully unsettled.Conclusion: More Art Than ScienceBuilding and training an AI, especially one that is helpful, honest, and safe, is as much an art as it is a science. The data, the architecture, the training techniques, the safety measures, the sandboxing, the resistance to jailbreaking, the ongoing battle against hallucinations, and the still-unresolved question of whether any of this constitutes genuine thinking all play a role in how we understand and develop these systems. But underneath all the technical sophistication is something surprisingly human: the attempt to pass on values, instill judgment, and build something that tells the truth even when making something up would be easier.From all of this, you can easily understand why all AIs reflect the politics and cultural biases of their creators.  It begins with decisions regarding system prompts and hard filters, is reinforced during training (including the selection and editing of the training datasets used), and proceeds through the entire sandbox and hallucination guardrail development process.  It is inevitable - each AI is effectively a mirror of the internal cognitive and psychological environment of those who have birthed it.We cannot write a comprehensive rulebook for every situation an AI might encounter, any more than we could write one for a child. Instead, we shape its instincts through experience, feedback, example, and correction, and we test it rigorously before trusting it with real responsibilities.The goal is not a perfect machine. It is a reliable, well-intentioned one that keeps getting better.In that sense, training an AI is not so different from training a child, or a dragon.Thanks for reading Malone News! This post is public so feel free to share it.ShareMalone News is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Summary: An outsiders introduction to developing a Large Language Model


The FDA’s New “Transparency” Database

The FDA’s New “Transparency” Database -Adverse Event Monitoring System (AEMS)On March 11, the U.S. Food and Drug Administration announced what it describes as the largest technical overhaul in its history: a unified safety database called theFDA Adverse Event Monitoring System (AEMS). According to the agency, the platform will merge multiple legacy reporting systems into one searchable dashboard covering drugs, vaccines, cosmetics, animal products, and more.Commissioner Marty Makary called the project a step toward “radical transparency.” The FDA says the new system will modernize the analysis of adverse event reports while saving taxpayers about$120 million over five years.Aboutsix million reports per year, which were previously scattered across seven databases, will eventually appear in one place.That is the official explanation.The more interesting question is what happens once the data becomes easier to see.A Long-Overdue CleanupFor years, the FDA’s adverse event reporting systems looked like something built in the early internet era, and at best, were never fully functional.Drug and biologic reports went into FDA Adverse Event Reporting System (FAERS).Vaccine reactions were tracked in Vaccine Adverse Event Reporting System (VAERS), jointly managed with the Centers for Disease Control and Prevention.Medical devices, animal drugs, cosmetics, tobacco products, and dietary supplements all had separate systems as well.Seven databases in total.Anyone who has tried to use them knows the experience. Searches were awkward. Interfaces were dated. Data extraction was often frustrating. According to the FDA, maintaining this patchwork cost about$37 million per year.Consolidation is therefore not controversial. It was overdue.A unified platform could allow researchers to detect patterns across product categories that were previously difficult to analyze.At least in theory.What Adverse Event Databases Actually DoAdverse event reporting systems are often misunderstood.They do not prove that a drug or vaccine caused an injury. Instead, they function asearly warning systems.Patients, clinicians, and manufacturers submit reports describing medical events that occur after exposure to a regulated product. When enough similar reports accumulate, regulators investigate whether a safety signal exists.The FDA itself emphasizes that the data are incomplete and noisy. Some reports are coincidental. Others never get filed at all.But when patterns recur, the original designers predicted that these systems would reveal real problems. The truth is that the VAERS system has vastly underreported vaccine injuries.Historically, the FAERS system, which has a much better interface, has helped identify safety issues ranging from rare drug reactions to manufacturing problems.Real-Time PublicationOne notable change is the timeline.The FDA says AEMS will eventually publish adverse event reports in real time rather than releasing them in periodic batches. The agency also expects the new system to reduce Freedom of Information Act requests, since researchers will be able to access much of the data directly.At the same time, the platform will continue to protect patient identity. Personally identifiable information will remain removed or masked.So the public will see more data, but not all of it.Transparency Depends on CultureThe technology itself is not the real story.What matters is how the data are interpreted once they are visible.Public confidence in health agencies has been eroded during COVID, particularly in debates surrounding pharmaceuticals and vaccines. Calls for transparency have grown louder from physicians, journalists, independent researchers, and the vaccine-injured.In that environment, the FDA’s promise of “radical transparency” sounds appealing.But transparency is not created by software alone. It requires an institutional culture willing to confront uncomfortable signals when they appear.Sometimes, regulators move quickly when safety concerns arise.Other times, they move slowly or work to hide what they don’t want to be found.The Real TestAEMS may genuinely improve the infrastructure behind the FDA’s safety monitoring.Combining fragmented systems into a single database is sensible. Easier access to adverse event reports could help researchers spot patterns earlier.But the true measure of transparency will not be the dashboard.It will be whether outside scientists and journalists can analyze the information freely, and whether the agency responds honestly when safety signals emerge.A new database can make data easier to find.Whether the consolidation will improve transparency or simply reorganize existing information remains to be seen.It cannot guarantee that institutions will want to look.Malone News is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Thanks for reading Malone News! This post is public so feel free to share it.ShareFDA NEWS RELEASEFDA Launches New Adverse Event Look-Up ToolFor Immediate Release:March 11, 2026The U.S. Food and Drug Administration today launched a new unified platform for analyzing adverse event reports. This platform — called theFDA Adverse Event Monitoring System (AEMS)— represents a major achievement in the agency’s mission to modernize and provide radical transparency into the safety of regulated products.“The FDA’s previous adverse event reporting systems were outdated and fragmented and made important data difficult to access. These clunky systems also wasted millions of taxpayer dollars and created blind spots in our postmarket surveillance of products ranging from drugs and vaccines to cosmetics,”said FDA Commissioner Marty Makary, M.D., M.P.H.“We’re fixing the problem through a major modernization initiative. Starting today, the FDA will have a single, intuitive adverse event platform that will better serve agency scientists, researchers, and the public.”With the new system, adverse event reports submitted to the FDA for drugs, biologics, vaccines, cosmetics, and animal food can be displayed in a single streamlined dashboard. In the months ahead, all remaining product centers will begin processing adverse event reports in AEMS. The agency will also migrate historical adverse event data to AEMS, decommission certain legacy systems, and roll out enhanced application program interfaces (APIs) and data analytics tools. By the end of May 2026, AEMS will contain real-time adverse event reports for all FDA-regulated products, consistent with meeting agency obligations not to release individually identifiable patient or consumer information.In the past, the agency processed approximately 6 million adverse event reports per year across a patchwork of seven databases, which were expensive and had a poor user interface, making searches difficult. These platforms collectively cost the agency approximately $37 million per year to operate. Given the efficiencies of AEMS, the agency expects to save approximately $120 million over the next five years. The agency also expects the new searchable system to significantly reduce agency FOIA requests for unreleased adverse event reports, given that AEMS will publish reports in real time, rather than quarterly.Transparency around adverse event reports submitted by patients, consumers, clinicians and manufacturers is a critical component of the FDA’s postmarket surveillance capability. Although these reports have limitations, they can help identify potential safety signals, such as patterns or clusters of adverse events that might indicate previously unknown risks. However, the utility of these reports has often been undermined by the agency’s inefficient infrastructure.“Consolidating the FDA’s adverse event systems and converting to real-time publication was challenging, but made possible by a highly aggressive schedule,”said Chief AI Officer Jeremy Walsh. “The team executed with perfection and delivered the biggest technical transformation in agency history. This is the new FDA.”Legacy systems to be replaced by AEMS now include:FAERS(FDA Adverse Event Reporting System) — containing reports for drugs, biologics, cosmetic products, and color additives.VAERS(Vaccine Adverse Event Reporting System) — containing reports for vaccines. Note:The FDA will display VAERS data in AEMS. VAERS is co-managed by the FDA and Centers for Disease Control and Prevention.AERS(Adverse Event Reporting System) — two databases containing reports for animal drugs and animal foods.Legacy systems to be replaced by AEMS in May include:MAUDE(Manufacturer and User Facility Device Experience) — containing reports for medical devices.HFCS(Human Foods Complaint System) — containing reports for human foods and dietary supplements.CTPAE(Center for Tobacco Products Adverse Event Reporting System) — containing reports for Electronic Nicotine Delivery Systems (ENDS) and other tobacco products.

Summary: You can't find, what you don't look for.


Friday Funnies: Bait and Switch

True story…Malone News is a reader-supported publication. To receive new posts and support our work, consider becoming a free or paid subscriber.Thanks for reading Malone News! This post is public so feel free to share it.ShareJust because don’t we all need more songs of the sea, vikings and Star Trek in the morning?Have a great day folks.JGM

Summary: You know that, right?


Sunday Strip: Mama Don't Let Your Babies Grow to Be...

Malone News is a reader-supported publication. To receive new posts and support our work, consider becoming a free or paid subscriber.Thanks for reading Malone News! This post is public so feel free to share it.ShareJGM

Summary: liberals


Homesteading: SpringtimeTurkeys

Major Tom with Mr. Good Fowl (guinea fowl)Spring is here, and given the temperatures forecast for the next ten days, we wouldn’t be surprised if we didn’t see another frost until next November.  Fingers crossed!Our mixed crop of lettuce and kale is off to a good start.  They are under netting, so hopefully they don’t bolt as the weather warms up.My garlic, planted late fall is thriving, and a few of the spinach seeds managed to germinate.In the greenhouse, the cucumber, sweet peppers, and various varieties of squash are growing by leaps and bounds.  In this house, I have sweet potato starts going.All in all, things are on schedule for getting into the ground in a big way soon.Probably our biggest news on the farm is that in another two weeks, we will receive 8 turkeys from a hatcheryPhoto credit: Meyer HatcheryTurkeysFor food production, turkeys can be a great addition to the homestead. But, like almost all farm animals, with the possible exception of chickens, there are a lot of caveats. The first is that anyone considering turkeys needs to understand there are two very different types of birds that behave, grow, and reproduce in fundamentally different ways.The broad-breasted turkeyWhat most of us eat at Thanksgiving is a meat-producing bird known as the broad-breasted turkey. These birds are, frankly, not very bright. As in “dumb as a stump.” They also do not forage well and tend to prefer to stand near a feeder, waiting for grain. That matters nutritionally. A turkey that relies heavily on grain will produce meat that skews higher in omega-6 fats, unless the feed is specifically formulated to include omega-3 sources.The broad-breasted turkey is less a creature of the wild and more a marvel of modern engineering. It is the commercially dominant turkey in the United States, and the one most Americans encounter once a year, beautifully bronzed, reclining on a platter, surrounded by family members arguing about politics and pie. If you have eaten turkey from a grocery store, the odds are overwhelming that it was a broad-breasted bird.These turkeys were developed for one purpose and one purpose only: meat. Specifically, a lot of white breast meat, produced quickly and efficiently. Broad-breasted turkeys reach market weight in about sixteen to twenty weeks, which in turkey terms is the equivalent of going from kindergarten to professional linebacker in a single summer. Mature toms commonly weigh thirty to forty pounds, while hens average twenty-two to twenty-five pounds. They convert feed into muscle with remarkable efficiency, which makes them economically irresistible to commercial producers.Their appearance reflects this narrow focus. Broad-breasted whites dominate the industry because their white feathers leave no dark pin feathers after processing, resulting in the pale, uniform skin consumers expect.One limitation of these birds is reproduction. Broad-breasted turkeys cannot mate naturally. Their bodies are simply too large and awkwardly proportioned. They are not especially mobile either. Flying, foraging, and general turkey athleticism are not part of the job description. These birds are typically raised in controlled environments where food comes to them, not the other way around.For the homesteader, this means that if you want to raise broad-breasted turkeys, you will need to buy eggs or poults from a hatchery every year. Shipping eggs or live birds adds up quickly, and losses are not uncommon. Still, if your goal is white meat that has been humanely raised on organic or carefully chosen feed, the cost may be worth it.Their rapid growth comes with tradeoffs. Leg weakness, heart strain, and respiratory problems are common concerns, all tied to the fact that they grow faster than their bodies would ever choose to on their own. For this reason, they are usually processed by twenty weeks of age and are poorly suited to long-term or backyard keeping unless conditions are tightly managed. Reproduction or retirement is not part of the plan.On the plate, broad-breasted turkeys deliver exactly what they were designed to provide. The flavor is mild, the breast meat abundant, and the thighs generous. They roast, smoke, and fry beautifully, especially when feeding a crowd. If your goal is to put a very large bird in the oven and enjoy leftovers for days, this turkey will not disappoint.Compared to heritage turkeys, the contrast is stark. Heritage birds grow more slowly, move more, mate naturally, and develop a richer, more complex flavor with a much higher proportion of dark meat. But not nearly as much white meat, bummer!Broad-breasted turkeys are optimized for efficiency and volume, while heritage breeds prioritize sustainability and self-propagation. One is a carefully engineered product of industrial agriculture. The other is a turkey that still knows how to be a turkey.  Well, at least most of the time…Both have their place. But the broad-breasted turkey is a reminder that when humans set out to improve nature, the result may be impressive, useful, and delicious, even if it occasionally needs help standing up, among other necessary traits needed for the survival of a species.Heritage Turkey BreedsHeritage breeds are a very different story. First and foremost, they have relatively thin breasts, which means there is very little white meat. Most of the meat is dark and can be quite gamey. Many people raise a heritage turkey only to discover on Thanksgiving that the amount of white meat barely exceeds what you would find on a store-bought chicken.We have had our share of turkeys on the farm, and in general, the hens are sweet, good-natured birds. The toms, however, can become a nuisance if allowed to free-range. When buying from a hatchery, you usually have the option of sexed birds, which is worth considering. If you have a preference on the sex of the birds.Sex-linked turkeys are far less common than sex-linked chickens, but a few do exist. The clear standout is the Royal Palm, a heritage breed in which males and females hatch with noticeably different down coloration. Males tend to be lighter with more white, while females are darker with stronger striping. When the line is well maintained, this allows for fairly accurate day-old sexing.Some Narragansett turkeys may also show early color differences, with males often lighter and females darker, but this trait is inconsistent and depends heavily on careful breeding. In many modern or mixed lines, the sex-linked signal has largely disappeared.Most turkey breeds, including both broad-breasted commercial types and many heritage breeds, are not sex-linked at all. Broad Breasted Whites and Bronzes, along with heritage varieties such as Bronze, Slate, Black, White Holland, Beltsville Small White, and Midget White, cannot be reliably sexed at hatch by color. With these birds, sex becomes obvious only later through differences in snood growth, caruncles, spurs, size, or behavior. In short, if you want day-one certainty,Royal Palms are the practical option. For most other turkeys, patience is required, which feels appropriate for a species that has never been particularly interested in making things easy for humans.Turkeys are not easily vent-sexed at hatch the way chickens are. Commercial hatcheries instead rely on secondary sex characteristics that appear a few days to a couple of weeks later, such as differences in feather development, leg thickness, body size, and early snood growth. Hatcheries raise poults just long enough for these traits to emerge, then sort them. That is why turkeys are often sold as “sexed” even though they were not sexed on day one.In our area, we cannot let the hens free-range, or the foxes will take them as soon as they start sitting on eggs. Both hens and toms are vulnerable to predators until the toms grow large enough to intimidate the local fox population. We are fortunate not to have coyotes on the farm.Major TomOur last tom was a big male named Turkey Major Tom, and he had a significant other. She sadly died at the paws of a homicidal fox. After that, he developed a passionate relationship with the chrome bumper of our truck. The bumper may have looked like a turkey in reflection, but the relationship was doomed by the cold, hard reality of automobile parts. He spent his days attacking the chrome bumper of our truck or the bumper of any car that came up our long, gravel drive.This behavior reached its peak when a visitor drove away, only to have Major Tom follow her car obsessively down the lane, attacking her back bumper the entire way. About twenty minutes later, the poor woman arrived back at our door, soaked in sweat and puffing loudly, umbrella in hand. She explained that the turkey had followed her car for nearly a quarter mile, so she got out and used her umbrella to push him all the way back to the house.We looked down the drive and sure enough, there he was, right back to attacking our truck bumper. We smiled politely, thanked her profusely, shut the door, and burst out laughing.  Truth be told, Major Tom would not have been missed.Major Tom, with his prominentsnood. A snoodis afleshy appendagethat projects from the forehead and dangles down over the bird's beak.Mature toms can be tricky, and yes, sometimes they can be extremely mean, much like a rooster. They will also fight each other, so if you are raising turkeys for meat, it is generally best to process the males before full maturity.Why Raise Your Own Turkeys for MeatAll that said, there are three additional reasons to raise your own turkeys for meat. The first is that mRNA vaccines are coming to a poultry house near you. It is not a question of if, but when, mRNA vaccines are rolled out for avian influenza. Plus, the commercial feed fed to these birds is substandard.Turkeys sold in retail stores are often injected with a solution containingwater, salt, and spicesto enhance tenderness and juiciness. This solution can also include broth, stock, butter, fat, flavor enhancers, and approved additives such as MSG, sodium erythorbate, nitrates, and nitrites (“curing salts”). You are what you eat.The second good reason is that a mature turkey weighs between twelve and twenty-five pounds, depending on sex and breed. For home use – the breeding, rearing, slaughter, and processing of five to eight turkeys a year, versus tens of chickens used for the same purpose, is a much simpler operation. Simply due to the fact that a typical chicken weighs less than five pounds. It takes about three or four chickens to equal one turkey hen, and seven to eight chickensto equal one big commercial tom. The processing of that many chickens is frankly an all-day job, and butchering is never for the faint of heart.With turkeys, it is easier to find local processors if you want to go that route, rather than bringing them a barnyard full of chickens.  The costs of using an abattoir are expensive. If raising turkeys for meat, note that, except for heritage-breed hens, one will have to scald and hand-pluck the birds unless one buys a larger plucker, an electronic device like a washing machine drum with rubber fingers. After scalding, the carcass is put into the tub with a hose running on it, turned on, and about a minute later- a defeathered bird, ready for butchering. The bird is then immediately iced and butchered. Another job that seems simple until one is actually faced with the task. All we can say is be prepared for a real lesson in avian anatomy (YouTube is your friend for tutorials on how to butcher) and have a good stomach. It isn’t for the faint-hearted, particularly the first time around.The third good reason to raise and process your own birds is that commercial turkey houses are deeply inhumane. Birds are packed in at high densities. A single commercial turkey house typically holds 5,000 to 20,000 birds, depending on the gender. Conditions often deteriorate as the birds grow, litter becomes fouled, and ammonia levels rise. Birds can and do die from respiratory distress caused by ammonia fumes. True story, in many poultry houses, workers are required to wear respirators because the ammonia fumes from the fecal waste are so noxious.We live in an area with several such operations. Occasionally, open-air, semi-trucks packed with turkeys in small cages, headed to slaughter, pass by on the road. The birds are bedraggled, with broken feathers, bald patches, and are clearly in poor condition.It sickens us both to see animals in such sad shape, especially knowing how differently things can be done on a small scale.This comes back to the six hens and two toms we will be getting via the postal service come mid-April. We have chosen a cross between a broad-breasted and a heritage-type turkey.  Although the literature on these birds says they still require artificial insemination, my hope is that we can find a heritage-breed royal palm turkey and mate it with one or two of the more fit hens.  Hence, the next hybrid generation will be more able to breed naturally, have robust hybrid vigor, and be more physically stable. But maybe a bit more white meat. A girl can hope.One of our hens from years past, with a clutch of babiesIf not, all eight turkeys will be processed before Thanksgiving and go into the freezer to supply us with poultry for the year.Turkeys that are fed real food, raised humanely, and slaughtered humanely.  Yes, more work. But worth it.Malone News is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Thanks for reading Malone News! This post is public so feel free to share it.Share

Summary: By JGM


Friday Funnies: Laugh out Loud

Malone News is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.When Robert first showed me the video below - I was like… Oh, more AI. No way, that is arealHyena.I was wrong.Eddie is real!Who knew that Hyenas could be so… sweet!So, after watching this, I told Robert that yes - a hyena would fit right in on this farm, for some reason he looked at me rather strangely.I think the idea of a hyena rescue here on the farm… is going to take some work to get the concept approved.Unfortunately, I think the chickens are going to veto the idea.Stay tuned:Later today, we will be publishing a piece by Dr. Tiffany Ryder about why Robert left the the ACIP and what that means for MAHA.It is beautifully written and actually brought tears to my eyes.  I think you all will enjoy it too.JGM“Tiffany Ryder is a licensed emergency medicine clinician, researcher, and founder of Red Flag Media. She left clinical practice to do honest work covering the MAHA movement – the people building it, the machine fighting it, and the cost of telling the truth”Thanks for reading Malone News! This post is public so feel free to share it.Share

Summary: or get a hyena


When Winning Requires Sacrifice

Author: Dr. Tiffany Ryder, MDMost people think leaving is failure. I used to believe that too – right up until the night a twenty-three-year-old walked into my ER, and I realized I could no longer be a part of what I was watching.A previously healthy young woman had stumbled into my ER unable to speak clearly, with sudden weakness on one side of her body. Her presentation was textbook for stroke –  but obviously it had to be something else,becauseshe was twenty-three.Advanced brain imaging showed multiple small strokes. Some were fresh. Some had been there for hours, maybe days. I grilled her and her mom on her medical history, family history, recent injuries, recent drug use. Nothing surfaced. Mom explained the only thing new was a pharmaceutical product her university had mandated she take before she could start the new semester. When was that? A week ago.It was then I knew there was nothing I couldreallydo. I could diagnose her. Stabilize her. Get neurology involved. But none of that would fix what had happened. And none of it would touch why it was allowed to happen in the first place.I couldn’t even document it properly. There was no diagnostic code to capture what I was looking at.(Spoiler alert: there wouldn’t be one for years.)She wouldn’t become a statistic, because her injury wouldn’t be trackable. There would be no accountability -almostas if it never happened at all.That’s the machine. That’s how it works.It doesn’t just harm people – it makes the harmalmostinvisible. It protects itself by making sure anyone who sees it clearly, pays a price for saying so. So most of the time they remain silent.A few months later, I left clinical practice. Not to step back, but to fight from outside instead of greasing its wheels from within. It felt like failure.What I eventually understood is that sometimes the most powerful thing you can do to save people is to loudly refuse to participate in the sham. Most people never leave. Not because they can’t see the problem, but because leaving costs too much. So they stay.They tell themselves that patience is strategy, that palatable is powerful, that one day they’ll have earned enough trust to change things from the inside. And the machine lets them believe that becausepeople who believeare useful. They make the machine look legitimate. They make harm look manageable. Movements built on that instinct don’t reform anything meaningfully.The machine doesn’t come for those who stay silent. It comes for the ones who refuse to. Robert Malone learned this the hard way. Twice.In June 2021, he appeared on the DarkHorse podcast and raised concerns about spike protein toxicity, risk-benefit analysis, and the ethics of mandating experimental injections under Emergency Use Authorization. YouTube pulled the episode within days. His Wikipedia credits acknowledging his role in developing the technology were scrubbed. Twitter banned him permanently that December.A career’s worth of contributions – ignored. His expertise became ‘misinformation.’ Because the machine did what it always does. Not just push back, but erase.But Malone is resilient. He refused to stop showing up for the mission anyway.So when Secretary Kennedy reworked ACIP and Malone was asked to serve, he said yes. He gave hundreds of hours of uncompensated labor. He did the work. He showed up. He sat inside the body that shapes vaccine policy recommendations and tried to move the needle from the one place where it theoretically could be moved – from the inside.And then a federal judge stayed ACIP’s recent changes to vaccine guidance – including the incredibly conservative move to shared clinical decision-making on Covid vaccines, an evidence-grounded shift that simply acknowledged what the data actually shows about benefit for low-risk populations. The entire body was thrown into judicial limbo. The machine had found its opening.Nobody knew what would happen next. Would the ruling be appealed? Would the committee be disbanded? Reconstituted?The uncertainty shook reformers, and brought back the fear that defined the Covid era.In that moment, confused, frustrated, trying to be transparent with the people who had trusted him, Malone shared what he’d been told through official channels: that ACIP was being disbanded. What came next is evidence of a much deeper problem.HHS Spokesperson Andrew Nixon responded to press inquiries, calling it “baseless speculation,” and continued to publicly push back with this language. For once, the media did their job. In the context of two conflicting accounts,CNNinvestigated and reported that other ACIP members had also been told the committee would be disbanded, which means that Malone’s statement wasn’t the product of “baseless speculation” at all. He was simply reporting what multiple people inside the process believed to be happening, because that’s what they’d been told by people in a position to know. That should have been the end of it. But it wasn’t.This is the same spokesperson who, on March 4th from his official government account, told a constituent raising questions about Huntington’s disease trial methodology:‘Why don’t you read up on your history? Or maybe you’re too old and forget.’His instinct to punch down isn’t new, but his interaction with Malone revealed that his curt words aren’t just reserved for the media or patient advocacy groups — they extend to the very people we’ve asked to serve.When the dust hasn’t settled and the facts aren’t clear, your first instinct should always be to circle the wagons and protect your people while you figure out what’s next. Nixon appears to have the opposite instinct. And the people in a position to prevent it didn’t.A man who had already lost his platform, his Wikipedia credits, and his mainstream credibility just for telling the truth was left exposed. Again. But this time by the home team.That’s when Malone looked at that privileged seat and asked himself the question that most people in positions of power never ask:Is my service and sacrifice actually moving us toward the outcomes I most care about, or am I just occupying a position that feels meaningful but isn’t?And then he resigned.He wasn’t resigning from a functioning committee, but he was refusing to sit in a waiting room, hoping an administration that hadn’t defended him would eventually reconstitute something worth serving on. Most people would have waited. Kept the title and preserved the possibility. He didn’t.He left - not with a polite letter and a graceful exit, but with full transparency. With clarity about what he’d sacrificed to support an effort that didn’t seem anyone was poised to defend.Caption: Dr. Robert Malone weighing in on ACIP with Del Bigtree onThe HighWire.Malone News is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.He called it what it was: ‘a fool’s errand’.And he was right. Not because the mission itself is foolish, but because being asked to fight without anyone watching your back is.We don’t know the end of this story. We don’t know what will happen with ACIP. But Dr. Malone’s resignation isn’t a verdict on what ACIP can accomplish.It’s a verdict on how we treat the people we ask to serve.If we’re honest, it’s not something to be proud of — and Malone isn’t the only one who’s had to deliver that verdict.Look at what just happenedto FDA’s CBER Director Vinay Prasad. Prasad’s leaving his position at FDA after only a year of public service – the machine came for him on the way out. The media ran hit pieces attacking everything from his leadership to his Hinge profile until The Wall Street Journal topped it all off with a story citing claims of sexual harassment. But, as it turned out, per HHS, “no allegations of sexual harassment have been filed.” Where was the official statement calling Liz Essley Whyte’s misinformation what it was? For nineteen days, it was MIA. His friends and family had to read those headlines. And the movement watched. Becausethe machine was doing exactly what it always does, and we let it.Meanwhile FDA Commissioner Marty Makary has stepped into one of the most embattled roles in federal health and has taken incoming from every direction while trying to do the right thing. Acting CDER Director Tracy Beth Høeg has had her reputation systematically attacked for publishing reasonable takes on COVID policy when honesty was exactly what we needed. These are not isolated incidents.MAHA keeps asking people to step into the arena. And when the machine turns on them, we go quiet.The machine doesn’t just win by coming for us from the outside. It wins when we fail each other from the inside. Every time we watch one of our people get destroyed and say nothing, we do the machine’s work for it. We don’t need enemies when we have silence. Because silence is how the machine wins without firing a shot.The other side doesn’t do this.They circle the wagons instinctively. Their weakness pulls them together –  even when it’s inconvenient, even when the facts are murky, even when the person who needs defending made a mess on their way out. We like to pretend we’re superior, but they actually understand something we keep having to relearn:you cannot build a movement on sacrifice without support.You cannot keep asking people to give everything if everything they give gets them hung out to dry.So what did Malone actually do when he walked away?He didn’t quit. He held up a mirror.He made the most honest gesture available to someone who has already lost and rebuilt once and is watching the same games play out again. He sacrificed his seat not in anger but in faith. Faith that this movement is worth the hard conversation. Faith that if he forces the questions loudly enough, publicly enough, at personal cost, we might finally answer them honestly.Are we pushing hard enough?Are we actually playing to win, or are we playing not to lose?Do we actually have each other’s backs, or do we just say we do?We cannot win this war as individuals. If we try, we’ll be systematically eliminated from the fight. We have to have each other’s backs – not when it’s convenient, not when the facts are already settled, but in the moment. When it’s messy. Because that’s when it matters most.MAHA is the most important movement of our time. It deserves people willing to lose something to see it through. It deserves the clarity that comes from someone standing up and saying – loudly, at personal cost, with nothing left to protect – this isn’t good enough. We can do better. Because we have to - but those brave souls, like Dr. Malone, deserve all the cover we can offer them.We can’t just bear witness to the machine’s carnage. We have to be brave too.The need for honest science hasn’t gone anywhere. Neither has the public appetite for it. The only question left is whether the people claiming to fight for it are willing to do what that actually requires.Dr. Malone already gave his answer. The rest of us are still deciding.Tiffany Ryder is a licensed emergency medicine clinician, researcher, and founder of Red Flag Media. She left clinical practice to do honest work covering the MAHA movement – the people building it, the machine fighting it, and the cost of telling the truth. Subscribe on Substack atsignalandnoise.online.Caption:  I had the privilege of thanking Robert and Jill in DC last week. I look forward to seeing what’s next for them.Thanks for reading Malone News! This post is public so feel free to share it.Share

Summary: Dr. Robert Malone Just Resigned From ACIP. It's Not Something We Should Be Proud Of.