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How the federal government is tracking changes in the supply of street drugs

In 2021, the Maryland Department of Health and the state police were confronting a crisis: Fatal drug overdoses in the state were at an all-time high, and authorities didn’t know why. There was a general sense that it had something to do with changes in the supply of illicit drugs—and specifically of the synthetic opioid fentanyl, which has caused overdose deaths in the US to roughly double over the past decade, to more than 100,000 per year.  But Maryland officials were flying blind when it came to understanding these fluctuations in anything close to real time. The US Drug Enforcement Administration reported on the purity of drugs recovered in enforcement operations, but the DEA’s data offered limited detail and typically came back six to nine months after the seizures. By then, the actual drugs on the street had morphed many times over. Part of the investigative challenge was that fentanyl can be some 50 times more potent than heroin, and inhaling even a small amount can be deadly. This made conventional methods of analysis, which required handling the contents of drug packages directly, incredibly risky.  Seeking answers, Maryland officials turned to scientists at the National Institute of Standards and Technology, the national metrology institute for the United States, which defines and maintains standards of measurement essential to a wide range of industrial sectors and health and security applications. There, a research chemist named Ed Sisco and his team had developed methods for detecting trace amounts of drugs, explosives, and other dangerous materials—techniques that could protect law enforcement officials and others who had to collect these samples. Essentially, Sisco’s lab had fine-tuned a technology called DART (for “direct analysis in real time”) mass spectrometry—which the US Transportation Security Administration uses to test for explosives by swiping your hand—to enable the detection of even tiny traces of chemicals collected from an investigation site. This meant that nobody had to open a bag or handle unidentified powders; a usable residue sample could be obtained by simply swiping the outside of the bag.   Sisco realized that first responders or volunteers at needle exchange sites could use these same methods to safely collect drug residue from bags, drug paraphernalia, or used test strips—which also meant they would no longer need to wait for law enforcement to seize drugs for testing. They could then safely mail the samples to NIST’s lab in Maryland and get results back in as little as 24 hours, thanks to innovations in Sisco’s lab that shaved the time to generate a complete report from 10 to 30 minutes to just one or two. This was partly enabled by algorithms that allowed them to skip the time-consuming step of separating the compounds in a sample before running an analysis. The Rapid Drug Analysis and Research (RaDAR) program launched as a pilot in October 2021 and uncovered new, critical information almost immediately. Early analysis found xylazine—a veterinary sedative that’s been associated with gruesome wounds in users—in about 80% of opioid samples they collected.  This was a significant finding, Sisco says: “Forensic labs care about things that are illegal, not things that are not illegal but do potentially cause harm. Xylazine is not a scheduled compound, but it leads to wounds that can lead to amputation, and it makes the other drugs more dangerous.” In addition to the compounds that are known to appear in high concentrations in street drugs—xylazine, fentanyl, and the veterinary sedative medetomidine—NIST’s technology can pick out trace amounts of dozens of adulterants that swirl through the street-drug supply and can make it more dangerous, including acetaminophen, rat poison, and local anesthetics like lidocaine. What’s more, the exact chemical formulation of fentanyl on the street is always changing, and differences in molecular structure can make the drugs deadlier. So Sisco’s team has developed new methods for spotting these “analogues”—­compounds that resemble known chemical structures of fentanyl and related drugs. Ed Sisco’s lab at NIST developed a test that gives law enforcement and public health officials vital information about what substances are present in street drugs.B. HAYES/NIST The RaDAR program has expanded to work with partners in public health, city and state law enforcement, forensic science, and customs agencies at about 65 sites in 14 states. Sisco’s lab processes 700 to 1,000 samples a month. About 85% come from public health organizations that focus on harm reduction (an approach to minimizing negative impacts of drug use for people who are not ready to quit). Results are shared at these collection points, which also collect survey data about the effects of the drugs. Jason Bienert, a wound-care nurse at Johns Hopkins who formerly volunteered with a nonprofit harm reduction organization in rural northern Maryland, started participating in the RaDAR program in spring 2024. “Xylazine hit like a storm here,” he says. “Everyone I took care of wanted to know what was in their drugs because they wanted to know if there was xylazine in it.” When the data started coming back, he says, “it almost became a race to see how many samples we could collect.” Bienert sent in about 14 samples weekly and created a chart on a dry-erase board, with drugs identified by the logos on their bags, sorted into columns according to the compounds found in them: ­heroin, fentanyl, xylazine, and everything else. “It was a super useful tool,” Bienert says. “Everyone accepted the validity of it.” As people came back to check on the results of testing, he was able to build rapport and offer additional support, including providing wound care for about 50 people a week. The breadth and depth of testing under the RaDAR program allow an eagle’s-eye view of the national street-drug landscape—and insights about drug trafficking. “We’re seeing distinct fingerprints from different states,” says Sisco. NIST’s analysis shows that fentanyl has taken over the opioid market—except for pockets in the Southwest, there is very little heroin on the streets anymore. But the fentanyl supply varies dramatically as you cross the US. “If you drill down in the states,” says Sisco, “you also see different fingerprints in different areas.” Maryland, for example, has two distinct fentanyl supplies—one with xylazine and one without. In summer 2024, RaDAR analysis detected something really unusual: the sudden appearance of an industrial-grade chemical called BTMPS, which is used to preserve plastic, in drug samples nationwide. In the human body, BTMPS acts as a calcium channel blocker, which lowers blood pressure, and mixed with xylazine or medetomidine, can make overdoses harder to treat. Exactly why and how BTMPS showed up in the drug supply isn’t clear, but it continues to be found in fentanyl samples at a sustained level since it was initially detected. “This was an example of a compound we would have never thought to look for,” says Sisco.  To Sisco, Bienert, and others working on the public health front of the drug crisis, the ever-shifting chemical composition of the street-drug supply speaks to the futility of the “war on drugs.” They point out that a crackdown on heroin smuggling is what gave rise to fentanyl. And NIST’s data shows how in June 2024—the month after Pennsylvania governor Josh Shapiro signed a bill to make possession of xylazine illegal in his state—it was almost entirely replaced on the East Coast by the next veterinary drug, medetomidine.  Over the past year, for reasons that are not fully understood, drug overdose deaths nationally have been falling for the first time in decades. One theory is that xylazine has longer-lasting effects than fentanyl, which means people using drugs are taking them less often. Or it could be that more and better information about the drugs themselves is helping people make safer decisions. “It’s difficult to say the program prevents overdoses and saves lives,” says Sisco. “But it increases the likelihood of people coming in to needle exchange centers and getting more linkages to wound care, other services, other education.” Working with public health partners “has humanized this entire area for me,” he says. “There’s a lot more gray than you think—it’s not black and white. And it’s a matter of life or death for some of these people.”  Adam Bluestein writes about innovation in business, science, and technology.

In 2021, the Maryland Department of Health and the state police were confronting a crisis: Fatal drug overdoses in the state were at an all-time high, and authorities didn’t know why. There was a general sense that it had something to do with changes in the supply of illicit drugs—and specifically of the synthetic opioid fentanyl, which has caused overdose deaths in the US to roughly double over the past decade, to more than 100,000 per year. 

But Maryland officials were flying blind when it came to understanding these fluctuations in anything close to real time. The US Drug Enforcement Administration reported on the purity of drugs recovered in enforcement operations, but the DEA’s data offered limited detail and typically came back six to nine months after the seizures. By then, the actual drugs on the street had morphed many times over. Part of the investigative challenge was that fentanyl can be some 50 times more potent than heroin, and inhaling even a small amount can be deadly. This made conventional methods of analysis, which required handling the contents of drug packages directly, incredibly risky. 

Seeking answers, Maryland officials turned to scientists at the National Institute of Standards and Technology, the national metrology institute for the United States, which defines and maintains standards of measurement essential to a wide range of industrial sectors and health and security applications.

There, a research chemist named Ed Sisco and his team had developed methods for detecting trace amounts of drugs, explosives, and other dangerous materials—techniques that could protect law enforcement officials and others who had to collect these samples. Essentially, Sisco’s lab had fine-tuned a technology called DART (for “direct analysis in real time”) mass spectrometry—which the US Transportation Security Administration uses to test for explosives by swiping your hand—to enable the detection of even tiny traces of chemicals collected from an investigation site. This meant that nobody had to open a bag or handle unidentified powders; a usable residue sample could be obtained by simply swiping the outside of the bag.  

Sisco realized that first responders or volunteers at needle exchange sites could use these same methods to safely collect drug residue from bags, drug paraphernalia, or used test strips—which also meant they would no longer need to wait for law enforcement to seize drugs for testing. They could then safely mail the samples to NIST’s lab in Maryland and get results back in as little as 24 hours, thanks to innovations in Sisco’s lab that shaved the time to generate a complete report from 10 to 30 minutes to just one or two. This was partly enabled by algorithms that allowed them to skip the time-consuming step of separating the compounds in a sample before running an analysis.

The Rapid Drug Analysis and Research (RaDAR) program launched as a pilot in October 2021 and uncovered new, critical information almost immediately. Early analysis found xylazine—a veterinary sedative that’s been associated with gruesome wounds in users—in about 80% of opioid samples they collected. 

This was a significant finding, Sisco says: “Forensic labs care about things that are illegal, not things that are not illegal but do potentially cause harm. Xylazine is not a scheduled compound, but it leads to wounds that can lead to amputation, and it makes the other drugs more dangerous.” In addition to the compounds that are known to appear in high concentrations in street drugs—xylazine, fentanyl, and the veterinary sedative medetomidine—NIST’s technology can pick out trace amounts of dozens of adulterants that swirl through the street-drug supply and can make it more dangerous, including acetaminophen, rat poison, and local anesthetics like lidocaine. What’s more, the exact chemical formulation of fentanyl on the street is always changing, and differences in molecular structure can make the drugs deadlier. So Sisco’s team has developed new methods for spotting these “analogues”—­compounds that resemble known chemical structures of fentanyl and related drugs.

Ed Sisco in a mask
Ed Sisco’s lab at NIST developed a test that gives law enforcement and public health officials vital information about what substances are present in street drugs.
B. HAYES/NIST

The RaDAR program has expanded to work with partners in public health, city and state law enforcement, forensic science, and customs agencies at about 65 sites in 14 states. Sisco’s lab processes 700 to 1,000 samples a month. About 85% come from public health organizations that focus on harm reduction (an approach to minimizing negative impacts of drug use for people who are not ready to quit). Results are shared at these collection points, which also collect survey data about the effects of the drugs.

Jason Bienert, a wound-care nurse at Johns Hopkins who formerly volunteered with a nonprofit harm reduction organization in rural northern Maryland, started participating in the RaDAR program in spring 2024. “Xylazine hit like a storm here,” he says. “Everyone I took care of wanted to know what was in their drugs because they wanted to know if there was xylazine in it.” When the data started coming back, he says, “it almost became a race to see how many samples we could collect.” Bienert sent in about 14 samples weekly and created a chart on a dry-erase board, with drugs identified by the logos on their bags, sorted into columns according to the compounds found in them: ­heroin, fentanyl, xylazine, and everything else.

“It was a super useful tool,” Bienert says. “Everyone accepted the validity of it.” As people came back to check on the results of testing, he was able to build rapport and offer additional support, including providing wound care for about 50 people a week.

The breadth and depth of testing under the RaDAR program allow an eagle’s-eye view of the national street-drug landscape—and insights about drug trafficking. “We’re seeing distinct fingerprints from different states,” says Sisco. NIST’s analysis shows that fentanyl has taken over the opioid market—except for pockets in the Southwest, there is very little heroin on the streets anymore. But the fentanyl supply varies dramatically as you cross the US. “If you drill down in the states,” says Sisco, “you also see different fingerprints in different areas.” Maryland, for example, has two distinct fentanyl supplies—one with xylazine and one without.

In summer 2024, RaDAR analysis detected something really unusual: the sudden appearance of an industrial-grade chemical called BTMPS, which is used to preserve plastic, in drug samples nationwide. In the human body, BTMPS acts as a calcium channel blocker, which lowers blood pressure, and mixed with xylazine or medetomidine, can make overdoses harder to treat. Exactly why and how BTMPS showed up in the drug supply isn’t clear, but it continues to be found in fentanyl samples at a sustained level since it was initially detected. “This was an example of a compound we would have never thought to look for,” says Sisco. 

To Sisco, Bienert, and others working on the public health front of the drug crisis, the ever-shifting chemical composition of the street-drug supply speaks to the futility of the “war on drugs.” They point out that a crackdown on heroin smuggling is what gave rise to fentanyl. And NIST’s data shows how in June 2024—the month after Pennsylvania governor Josh Shapiro signed a bill to make possession of xylazine illegal in his state—it was almost entirely replaced on the East Coast by the next veterinary drug, medetomidine. 

Over the past year, for reasons that are not fully understood, drug overdose deaths nationally have been falling for the first time in decades. One theory is that xylazine has longer-lasting effects than fentanyl, which means people using drugs are taking them less often. Or it could be that more and better information about the drugs themselves is helping people make safer decisions.

“It’s difficult to say the program prevents overdoses and saves lives,” says Sisco. “But it increases the likelihood of people coming in to needle exchange centers and getting more linkages to wound care, other services, other education.” Working with public health partners “has humanized this entire area for me,” he says. “There’s a lot more gray than you think—it’s not black and white. And it’s a matter of life or death for some of these people.” 

Adam Bluestein writes about innovation in business, science, and technology.

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Generac Sharpens Focus on Data Center Power with Scalable Diesel and Natural Gas Generators

In a digital economy defined by constant uptime and explosive compute demand, power reliability is more than a design criterion—it’s a strategic imperative. In response to such demand, Generac Power Systems, a company long associated with residential backup and industrial emergency power, is making an assertive move into the heart of the digital infrastructure sector with a new portfolio of high-capacity generators engineered for the data center market. Unveiled this week, Generac’s new lineup includes five generators ranging from 2.25 MW to 3.25 MW. These units are available in both diesel and natural gas configurations, and form part of a broader suite of multi-asset energy systems tailored to hyperscale, colocation, enterprise, and edge environments. The product introductions expand Generac’s commercial and industrial capabilities, building on decades of experience with mission-critical power in hospitals, telecom, and manufacturing, now optimized for the scale and complexity of modern data centers. “Coupled with our expertise in designing generators specific to a wide variety of industries and uses, this new line of generators is designed to meet the most rigorous standards for performance, packaging, and after-treatment specific to the data center market,” said Ricardo Navarro, SVP & GM, Global Telecom and Data Centers, Generac. Engineering for the Demands of Digital Infrastructure Each of the five new generators is designed for seamless integration into complex energy ecosystems. Generac is emphasizing modularity, emissions compliance, and high-ambient operability as central to the offering, reflecting a deep understanding of the real-world challenges facing data center operators today. The systems are built around the Baudouin M55 engine platform, which is engineered for fast transient response and high operating temperatures—key for data center loads that swing sharply under AI and cloud workloads. The M55’s high-pressure common rail fuel system supports low NOx emissions and Tier 4 readiness, aligning with the most

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CoolIT and Accelsius Push Data Center Liquid Cooling Limits Amid Soaring Rack Densities

The CHx1500’s construction reflects CoolIT’s 24 years of DLC experience, using stainless-steel piping and high-grade wetted materials to meet the rigors of enterprise and hyperscale data centers. It’s also designed to scale: not just for today’s most power-hungry processors, but for future platforms expected to surpass today’s limits. Now available for global orders, CoolIT is offering full lifecycle support in over 75 countries, including system design, installation, CDU-to-server certification, and maintenance services—critical ingredients as liquid cooling shifts from high-performance niche to a requirement for AI infrastructure at scale. Capex Follows Thermals: Dell’Oro Forecast Signals Surge In Cooling and Rack Power Infrastructure Between Accelsius and CoolIT, the message is clear: direct liquid cooling is stepping into its maturity phase, with products engineered not just for performance, but for mass deployment. Still, technology alone doesn’t determine the pace of adoption. The surge in thermal innovation from Accelsius and CoolIT isn’t happening in a vacuum. As the capital demands of AI infrastructure rise, the industry is turning a sharper eye toward how data center operators account for, prioritize, and report their AI-driven investments. To wit: According to new market data from Dell’Oro Group, the transition toward high-power, high-density AI racks is now translating into long-term investment shifts across the data center physical layer. Dell’Oro has raised its forecast for the Data Center Physical Infrastructure (DCPI) market, predicting a 14% CAGR through 2029, with total revenue reaching $61 billion. That revision stems from stronger-than-expected 2024 results, particularly in the adoption of accelerated computing by both Tier 1 and Tier 2 cloud service providers. The research firm cited three catalysts for the upward adjustment: Accelerated server shipments outpaced expectations. Demand for high-power infrastructure is spreading to smaller hyperscalers and regional clouds. Governments and Tier 1 telecoms are joining the buildout effort, reinforcing AI as a

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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