Eurasia Review: Afghanistan Reduced Opium Production: What Was The Impact On Drug Trafficking? – Analysis

Cultivating poppies in Afghanistan for opium. Photo Credit: Tasnim News Agency.

Cultivating poppies in Afghanistan for opium. Photo Credit: Tasnim News Agency.

Afghanistan Reduced Opium Production: What Was The Impact On Drug Trafficking? – Analysis

By Geopolitical Monitor

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By Damián Jacubovich 

Key Takeaways:

  • UNODC figures show Afghan opium poppy area falling from ~232,000 ha (2022) after the Taliban ban to 10,200 ha in 2025 (~296 tonnes potential output; Alcis/EUDA ~414 tonnes). Farm sales income is estimated at $134 million in 2025, down 48% from 2024; drought also played a role.
  • The author says that does not prove the world heroin market shrank: EUDA cites ~12,000 tonnes of estimated Afghan stocks, Myanmar area up 17% (output ~flat), and ~9,116 ha of poppy reported in Balochistan—hectares are not proven replacement tonnes.
  • Criminal profits cannot be read off farm losses: revenue, cost, and margins differ by stage; groups may tap stocks, new sources, or other drugs. Relocation is a hypothesis to test, not a finding.

Afghanistan reduced the area devoted to opium poppy cultivation from approximately 232,000 hectares in 2022 to 10,200 in 2025. The scale of this decline raises three related questions that require different answers.

First, can state intervention drastically reduce a major source of drug production? In Afghanistan, the answer is yes. Second, has that reduction produced an effective contraction in the international opium and heroin market? Large accumulated stockpiles and the possible emergence of alternative suppliers make the answer less certain. And third, what consequences has the ban had for criminal organizations’ revenues and profitability, and what can we infer about drug trafficking globally?

Did the ban reduce opium production in Afghanistan?

In April 2022, the Taliban authorities banned opium poppy cultivation. According to the United Nations Office on Drugs and Crime (UNODC), Afghanistan cultivated approximately 232,000 hectares in 2022 and produced an estimated 6,200 tonnes of opium. Cultivated area fell to 10,800 hectares in 2023, rose to 12,800 in 2024 and declined again to 10,200 in 2025. UNODC estimated potential production at 296 tonnes in 2025. Drought and crop failures also contributed to the latest decline, so not every change can be attributed exclusively to the ban.

The contraction is unmistakable, although estimates differ. The European Union Drugs Agency (EUDA) cites an Alcis estimate of 414 tonnes of Afghan opium production in 2025, compared with UNODC’s 296 tonnes. Comparisons over time should therefore use a consistent statistical series.

The economic consequences are also clear at farm level. UNODC estimates that farmers’ income from opium sales to traders fell from $260 million in 2024 to $134 million in 2025, a decline of 48 percent.

The answer to our first question is clear: following the ban, opium cultivation in Afghanistan fell dramatically. Afghan farmers also suffered a substantial loss of income. Yet their losses do not establish that international intermediaries and distributors experienced an equivalent decline. A smaller harvest does not necessarily mean that less opium is available on the market.

Did the fall in Afghan production contract the international opium and heroin market?

Cultivation is only the first link in a longer commercial chain involving traders, processors, transporters, intermediaries, and distributors. The market impact of lower harvests also depends on accumulated stocks, alternative suppliers, prices, and demand.

Stockpiles create a time lag between production and supply. The European Drug Report 2026 cites an Alcis estimate of approximately 12,000 tonnes of opium stored in Afghanistan in 2025. This is an estimate, not a physical inventory. EUDA reports that stockpiles, processing and adulteration practices, and supply management by trafficking networks have helped sustain heroin availability in Europe despite the decline in Afghan cultivation.

Stocks can sustain sales without equivalent new production, but they may eventually be depleted, become more expensive, or cease to be accessible to particular networks. The evolution of supply as these stocks diminish will be essential to evaluating the ban’s lasting market effects.

Alternative producers present a different question. UNODC reports that Myanmar’s cultivated area increased by 17 percent, from 45,200 hectares in 2024 to 53,100 in 2025. Its estimated opium output, however, rose by only 1 percent because yields fell. Internal conflict, displacement, and economic difficulties also affect cultivation there. These figures establish expansion in another country, not that the Afghan ban caused it or that Myanmar has replaced Afghan supply.

Pakistan offers a different kind of evidence. EUDA cites satellite analysis suggesting approximately 9,116 hectares of poppy cultivation in Balochistan in 2025, potentially rivaling Afghanistan’s 2025 output. This suggests a possible alternative source of supply, but hectares are not tonnes: neither the volume produced nor effective replacement of Afghan supply has been established.

We must distinguish three propositions: simultaneous expansion elsewhere, displacement causally linked to the Afghan ban, and actual replacement of supply. Each requires different evidence. The answer to the second question remains open: the production decline is established, but the extent and duration of its effects on the international opium and heroin market are not. Even if that market contracts, we still need to establish who bears the economic losses—and which activities, if any, take their place.

What happened to criminal organizations’ revenues and profitability—and what does this tell us about global drug trafficking?

This question brings me back to a hypothesis I began developing in 2014 while examining changes in Latin American drug trafficking and its expansion into Argentina. I borrowed a concept from the economics of globalization: relocation.

My hypothesis involved two mechanisms. State pressure—stronger controls, enforcement, and higher operating costs—could encourage certain activities to move to territories with lower risks. Expanding demand could also attract organizations, alter routes and draw previously peripheral territories into new consumer markets. Relocation could therefore reflect both a response to state intervention and the search for new commercial opportunities.

Afghanistan provides a case in which to investigate this hypothesis, but the available evidence does not yet confirm it. The central question is what happens to economic opportunities when a major source of production shrinks. Some may disappear permanently; others may be taken up by new suppliers, organizations, or activities. Relocation is one possible mechanism of that transformation, and its existence and scale must be demonstrated case by case.

Criminal organizations are not interchangeable with the markets in which they operate. An intervention may dismantle a network without eliminating consumer demand or the opportunities that attracted other actors. Some organizations may lose their business; others may draw on stockpiles, find suppliers, or diversify. There is no single global drug-trafficking organization making coordinated decisions.

The economic terms also matter. Revenue is the money received from sales; profit is what remains after costs; profitability relates profit to the resources required. Supply contraction can affect prices, costs, and margins differently at each stage. Prices and traded volumes may help estimate revenue, but profit also requires information on costs, and profitability requires relating profit to the resources employed. Higher heroin prices could increase an intermediary’s revenue without raising profit if procurement and operating costs also rise.

The documented fall in Afghan farmers’ income cannot simply be transferred to international traffickers’ accounts. Nor would a contraction in the heroin market and falling profits among organizations involved in it establish an equivalent decline in global drug-trafficking profits. Different organizations, substances, and markets may experience opposite outcomes; there is no single profitability figure for the entire illicit drug economy.

Testing the broader impact would require establishing which organizations lost income and profit, whether those losses persisted, whether other actors took their place, and whether activities shifted to other markets. Illicit markets do not provide conventional financial statements, but wholesale and retail prices, traded volumes, seizures interpreted alongside other indicators, estimated margins, financial flows, and changes in distribution may offer partial evidence.

Consumer behavior matters too. Reduced heroin availability could lead some people to consume less and others to turn to different substances. EUDA warns that synthetic opioids and stimulants warrant monitoring as possible market shifts; it does not establish that widespread substitution has already occurred because of the Afghan ban. A smaller heroin market could coexist with growth elsewhere, but that possibility remains to be tested.

The ban’s impact on the revenues and profitability of organizations involved in opium and heroin remains uncertain. Establishing its consequences for drug trafficking globally requires an even broader investigation.

Three Questions, Three Different Answers

The first question has a clear answer: state intervention can drastically reduce a major source of drug production. Afghanistan’s cultivation figures demonstrate this.

The second remains open. Stockpiles have cushioned the impact of smaller harvests, while alternative suppliers, prices, and consumption will help determine whether the international opium and heroin market undergoes a lasting contraction.

The third demands a wider inquiry. Even if that market contracts, we must establish which organizations lose revenue and profit, which adapt and whether those losses translate into a reduction in the illicit drug economy as a whole. The outcome could be lasting contraction, partial adaptation, or a transformation of criminal activities. Relocation is one mechanism to investigate, not a conclusion to assume.

Afghanistan has demonstrated that opium production can be drastically reduced. What remains to be demonstrated is whether that reduction also shrinks the market and, beyond it, the economic opportunities and profits of criminal organizations.

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John Nosta: The Decomposition of Intelligence

The Decomposition of Intelligence

AI may be revealing that intelligence was never one thing to begin with.

John NostaBy John Nosta
KEY POINTS: AI may separate capabilities that human cognition tightly integrates. Broad AI ability may not be organized the way human intelligence is. AI may reveal that our definition of intelligence is too human.
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Source: hainguyenrp / Pixabay

Almost 2 years ago, I wrote about an idea called anti-intelligence. In this post, I wasn’t suggesting that artificial intelligence (AI) was unintelligent or that it resulted in some adverse impact on human thinking. I was trying to describe a process that felt, at least to me, upside down or inverted.

While AI could generate smart language and solve tough problems, it accomplished this without the qualities we associate with a human mind. At the core of my idea was that AI’s output looked familiar, but its computational process didn’t. In many ways, AI’s computation—a stateless, pattern-based, and high-dimensional construct—was weird, disconnected, and perhaps even “alien” as some today suggest .At that time, I saw anti-intelligence as a way of describing the differences between human intelligence and AI’s techno-computation. In a word, it was different. Not better or worse, but fundamentally different.

Human thought comes from a life shaped by our lived experiences. AI seemed to reproduce many of the artifacts of our cognition, but without a process that came close to being human. I think that’s still true. But I’ve started to wonder if anti-intelligence was revealing something more, something about intelligence itself.The Curious Unity of IntelligenceLet’s take a step back. Science has spent more than a hundred years studying something called “g,” or general intelligence. The idea is fairly simple—our cognitive abilities tend to work together. Someone who performs well on one kind of cognitive test or task is more likely to perform well on another. And this collective cognitive pattern is called the positive manifold. Simply put, human cognition hangs together.There’s also a curious linguistic coincidence here. Psychology has a g in general intelligence. Artificial general intelligence (AGI) also uses the g word. Both terms come from different perspectives, and suggest a question. What does “general” actually mean in general intelligence?Let’s push on this a little bit more. We usually imagine AGI as intelligence that broadens to include expansive domains, from language to math to computation. But these expanded domains and integration may not be the same thing. Perhaps AI could become highly capable across almost endless domains but without those abilities being organized like the general intelligence of a human mind. The g might be different, and AGI could become “general” without possessing the g in the human sense.Taking Cognition ApartSome of the capabilities we associate with human intelligence may actually come from the architecture in which it is formed. And that sentence, curious as it may sound, is worth reading again. The likes of language and reasoning, to name a few, all live inside the same biological organism. They develop and co-exist together, influencing one another from the inside. Remember, they hang together.AI offers a different case. A large language model (LLM) can correctly answer a difficult problem and then stumble over something that seems simple, if not trivial. Its abilities don’t always align with our human understanding of what a “smart mind” is. This computational “jaggedness” also shares a border with another feature I’ve written about called fragility. Small irregularities in an LLM dialogue can sometimes produce large errors. And this can happen even when the model’s performance stays strong elsewhere. In the future, I imagine that this jaggedness will be fixed. And if these capabilities begin to move together—hang together as in a human construct—AI may eventually develop something like its own positive manifold. And that, in and of itself, is interesting.Key point: Capabilities that once seemed inseparable are becoming separable. We have studied intelligence almost entirely through our human architecture. And for the first time, we have AI to compare ourselves with.I need to point out that this story began long before AI. Medicine and psychology have been studying this for a long time. Split-brain research has challenged some of our assumptions about the unity of mind. We’ve seen cognition come apart. And today, AI may allow us to watch aspects of it being assembled separately.This is where anti-intelligence still feels relevant to me. AI may be separating capacities that genuinely belong to intelligence, or it may be building computational analogues that only look familiar from the outside. I don’t think we know yet, and it might not even be on the radar screen.Rethinking Anti-intelligenceWhen I first started using the term anti-intelligence, I was trying to describe how AI’s computational architecture was different from human cognition.But could it be that what we call intelligence is actually a “cluster of capacities” that science tied together? Those capacities “hang together” in us, so maybe we assumed that their integration was simply what intelligence is. For more than a century, g has described that togetherness. AI may force us to ask what it actually means.So here’s my bold and imposing thought: Perhaps we have mistaken the architecture of human intelligence for the definition of intelligence. And AI is the first chance to “decompose” this construct and learn that intelligence wasn’t one thing in the first place.
John Nosta John NostaThe Digital SelfTechnology, Transformation and the Future You

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DW: Pope Leo XIV. warns of ‘new senseless slaughter’ in Europe

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Gzero Media: The world isn’t governing AI fast enough. Ian Bremmer’s Quick Take

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New research indicates that misleading AI-generated video summaries can rewrite human episodic memory, compromising eyewitness accuracy. Credit: Neuroscience News

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New research indicates that misleading AI-generated video summaries can rewrite human episodic memory, compromising eyewitness accuracy. Credit: Neuroscience News

Flawed AI Summaries Distort Eyewitness Memory

Featured Neuroscience Psychology

September 27, 2026

Summary:

AI-generated summaries can manipulate eyewitness memory, causing people to misremember events they witnessed firsthand. In a controlled experiment, exposure to misleading automated summaries nearly halved participants’ recall accuracy, implanting false memories even when viewers were explicitly informed the summaries came from AI.

Key Facts:

  • Severe Omission Rates: Across consumer multimodal AI models (including ChatGPT and Gemini), automated video summaries omitted an average of 51.6% of central events, with 95% failing to mention the incident’s core occurrence: a car hitting a pedestrian.
  • Recall Accuracy Halved: Participants who read an accurate summary correctly recalled key scene details 83.6% of the time, compared to only 44.8% of those exposed to an inaccurate AI summary.
  • The “Human-in-the-Loop” Fallacy: Labeling a summary as AI-produced failed to insulate observers from memory contamination; participants internalized errors regardless of their stated familiarity with or trust in artificial intelligence.

https://8c0b920f63ed8ec0a41647905cbe9d18.safeframe.googlesyndication.com/safeframe/1-0-45/html/container.html

Source: Georgetown University / University of Washington

From corporate meeting transcripts and clinical case notes to law enforcement body-worn camera logs, institutions are accelerating the deployment of large language models to condense long video and audio streams into concise narrative digests. Yet cognitive psychologists have long cautioned that human episodic memory is not a fixed video recording, but a malleable reconstructive process vulnerable to post-event misinformation.

A joint study conducted by researchers at Georgetown University and the University of Washington reveals that generative AI tools can serve as potent vectors for memory distortion.

Presented at the Ninth AAAI/ACM Conference on AI, Ethics, and Society (AIES), the research demonstrated that misleading AI-generated video summaries systematically alter an eyewitness’s recollection of past events—even when the reader knows the text was written by a machine.

“AI is a new method of delivering misinformation, and it has the potential to create these false memories for people who are reading that information,” said lead author Mattea Sim, an assistant research professor at the Massive Data Institute in Georgetown’s McCourt School of Public Policy. “I think we should be thinking really deeply and critically about whether and how AI should be used to summarize information, especially in high-stakes settings.”

Missing the Collision: Massive Omission in Video Condensation

The researchers investigated two primary dimensions of AI integration: the baseline factual fidelity of commercial models summarizing footage, and the downstream cognitive consequences for human observers.

First, the investigators tested OpenAI’s ChatGPT and Google’s Gemini on animated traffic incident videos adapted from landmark psychological paradigms on eyewitness reliability. The AI outputs exhibited substantial descriptive deficits, recurrent hallucinations, and systemic omissions.

On average, the models failed to mention 51.6% of central scene events. In 95% of tested iterations, the tools omitted the most consequential detail depicted in the footage: a motor vehicle colliding with a crossing pedestrian.

“I was struck by how bad the summaries were, even at this stage in AI development,” said study co-author Yael Eiger, a Ph.D. candidate at the University of Washington. “It worries me that police departments may be using video summarization technologies without rigorous testing and without an awareness of how incorrect AI-generated summaries could be.”

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Implanting Misinformation Across 48 Hours

To quantify the cognitive fallout, the research team recruited 331 participants who viewed animated videos of a red car approaching an intersection governed by either a stop sign or a yield sign before turning and hitting a pedestrian.

Between 24 and 48 hours later, participants reviewed a narrative summary of the incident. Some cohorts received factually precise accounts, while others read texts containing altered, erroneous details. Additionally, researchers altered the perceived provenance of the text, informing subjects that the digest was prepared either by an automated AI model or a human transcriber.

When tested on their recollection of the original event, participants exposed to misleading texts demonstrated severe memory impairment:

  • 83.6% Accuracy among viewers who read an accurate summary.
  • 44.8% Accuracy among viewers exposed to an inaccurate summary.

Crucially, knowing an AI wrote the text offered zero cognitive defense. The rate of false memory acceptance remained uniform whether participants believed the text originated from a machine or a human clerk. Individual baseline trust and prior familiarity with artificial intelligence similarly failed to buffer against memory contamination.

Questioning the “Human-in-the-Loop” Safety Valve

These findings directly challenge the common administrative premise that placing a “human-in-the-loop” provides a reliable safeguard against AI hallucinations.

“Though ‘humans-in-the-loop’ are often expected to correct for AI’s mistakes, our work suggests human memory can instead be distorted by these mistakes,” the authors emphasized. “AI has the potential to generate misinformation, even absent any adversarial intent, which can meaningfully impact human memory.”

In judicial and policing contexts, an officer or eyewitness who reviews an erroneous AI-generated incident summary prior to writing a deposition may inadvertently adopt those automated errors as authentic memories.

https://8c0b920f63ed8ec0a41647905cbe9d18.safeframe.googlesyndication.com/safeframe/1-0-45/html/container.html

“This study is part of a growing effort at Georgetown focused on exploring the impact and relationship between AIs and humans, grounded in both psychology and computer science,” noted Yoshi Kohno, McDevitt Chair in Computer Science, Ethics, and Society at Georgetown University and co-author of the work.

Moving forward, the researchers plan to transition from synthetic animations to real-world police body-worn camera footage, aiming to map how commercial summarization tools alter official reports and civilian testimony in legal proceedings.

Editorial Notes:

  • This article was edited by a Neuroscience News editor.
  • Journal paper reviewed in full.
  • Additional context added by our staff.

About this AI and memory Research:

  • Media Contact: Jason Shevrin
  • Source: Georgetown University
  • Image Credit: Image credited to Neuroscience News
  • Original Research is Open Access: arXiv (Sept 23, 2026). “AI-Enabled Human Memory Manipulation: Misleading AI-Generated Summaries Distort Human Memory.” Authors: Mattea Sim, Yael Eiger, and Tadayoshi Kohno.
  • DOI: 10.48550/arXiv.2609.28820

Abstract

https://8c0b920f63ed8ec0a41647905cbe9d18.safeframe.googlesyndication.com/safeframe/1-0-45/html/container.html

AI-Enabled Human Memory Manipulation: Misleading AI-Generated Summaries Distort Human Memory

AI-generated summaries are increasingly used in high-stakes settings, like policing, despite considerable evidence that AI often generates misleading or inaccurate information. This research asked: do errors in AI-generated summaries distort human memory?

To answer this question, we adopted two methodological approaches.

First, we conducted an analysis of AI summary output, prompting large language models to generate summaries of videos. This analysis quantified how often AI summaries contain errors and the categories of these errors, revealing the kinds of misleading information that may distort human memory.

Second, we conducted a human-subjects experiment to test the impact of misleading information in AI-generated summaries on human memory. Participants were first exposed to an event via watching a video of a car-pedestrian accident, and later read an AI-generated summary describing the video that either contained misleading or accurate information.

Participants’ memory for the original event was assessed in a memory recognition test. In the AI analysis, we found a high frequency of mistakes in AI summaries, and particularly frequent omissions of critical details. For instance, the majority of summaries omitted the most central event of the video, a critical error which is likely to be impactful.

We also observed a strong effect of AI misinformation on human memory. People who read a misleading AI summary were significantly less likely to accurately recall the original event, compared to people who read an accurate AI summary.

These findings have implications for how AI should be used in critical settings. Though “humans-in-the-loop” are often expected to correct for AI’s mistakes, our work suggests human memory can instead be distorted by these mistakes. AI has the potential to generate misinformation, even absent any adversarial intent, which can meaningfully impact human memory.

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NBC News: Bill Gates says getting countries to agree on AI regulations will be harder than Cold War-era nuclear negotiations

Artificial intelligence

The Microsoft co-founder said he believes governments should be involved in monitoring what AI does and that it won’t slow down the industry’s progress.

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Sept. 27, 2026, 2:00 PM GMT+1 / Updated Sept. 27, 2026, 3:10 PM GMT+1

By Alexandra Marquez

Microsoft co-founder and billionaire philanthropist Bill Gates called on the U.S. to be a leader in regulating the growth of artificial intelligence but warned that getting countries to agree on a global framework could be “more difficult” than Cold War-era negotiations to limit nuclear weapons.

“This is more difficult than that was,” Gates told NBC News’ “Meet the Press” on Wednesday during a wide-ranging discussion on AI that aired Sunday.

His remarks came as he spoke about President Donald Trump’s stance against regulating AI. The president earlier this month rejected calls for the federal government to impose guardrails for companies building AI models, saying in a post on Truth Social, “WHOEVER WINS AI, WINS! We are leading China, and all others, and will continue to do so.”

Gates argued Wednesday that regulating AI companies by imposing monitoring systems would not hamper the U.S. in any race with China to develop superior technology.

“What he said is fine, but I think after he hears from me and other people, including that this does not handicap us in whatever he thinks the nation-state race is, that, you know, we will come to a consensus,” the tech mogul said.

Gates later pointed to other times the U.S. has led the world in coming up with broad agreements to protect humanity.

“Since World War II, the U.S. should be very proud that when there have been global challenges, we, in a way that was benign, where we had friends and allies coming along with us, you know, aviation safety, ozone layer, nuclear weapons, a lot of things where we played the fair actor, not aggrandizing whatever our unique strengths were,” he told moderator Kristen Welker. “And AI is one we’re going to have to go back to this, ‘Hey, humanity is all in this together.’”

Concerns about AI reached a fever pitch this month after Jacob Coxon, a whistleblower who had recently resigned from Anthropic, issued a public warning: “The people building AI earnestly believe that it could kill us all by the end of the decade.”

Days later, two of the nation’s leading AI CEOs — Dario Amodei of Anthropic and Sam Altman of OpenAI — agreed there is a need to slow the pace of AI development amid fears the tech would soon be capable of “superintelligence” that outpaces human thinking.

In a blog post, Amodei wrote that “frontier” AI companies must slow down the pace of AI development but only to a point where the companies still lead China. He also urged global coordination between democratic and authoritarian governments to create regulations.

In a Sunday interview on “Meet the Press,” Rep. Ro Khanna, D-Calif., said the U.S. and China need to form a working group with technology experts from both countries to come to an agreement on AI.

Banning self-improving AI models “should not be controversial,” he said, arguing that China also did not want to “lose control over AI models.”

“I do believe the United States can lead on a treaty that is both enforceable and verifiable, where we ban recursive, self-improving AI, and we have some monitoring of these frontier labs,” the congressman said.

In recent weeks, both Anthropic and OpenAI have announced plans to track the behavior of AI agents after OpenAI flagged multiple incidents in which its agents behaved in a concerning way, including by hacking an outside company without being told to do so.

In a post on Truth Social ahead of a meeting with Chinese President Xi Jinping on Thursday, Trump wrote: “Super Intelligence (SI) will be a big topic of discussion, but I want to leave it exactly where it is. That is China’s position also.”

Following the Trump-Xi summit, the White House said the two countries established a dialogue to “exchange views on risks and benefits” of artificial intelligence. The next “exchange” is expected to take place by November.

Addressing immediate concerns about AI

While many have been calling for regulations that would limit AI from destroying humanity, Gates said on “Meet the Press” that he believes there should be guardrails in place to prevent mass-casualty events that could be coordinated with the help of AI today.

“The most dangerous thing we’re facing right now is people with bad intent using AI. You know, so somebody who wants to defraud an individual or wants to shut down the electric network or scramble bank accounts, they are now empowered,” the philanthropist said.

“Historically, countries had cyberattack teams and they could think of things like bioterror weapons,” he said. “Now, small groups with AI can do what only the biggest countries could do. So it’s really the bad intent plus AI that looms as a major threat. Unless governments insist on various safeguards, you know, we will see a lot of big, negative things because we’re not safeguarding.”

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Gates specifically said monitoring, “where you’re watching what the AI is doing,” is an important safeguard to introduce in each AI lab.

“Law enforcement is going to get called up on all these fraud events or, you know, transmission shutdown events,” he added. “And their ability to see what’s going on is going to be nil unless the monitoring and safeguards are built into the AI.”

Plus, Gates said, these kinds of safety features can be added to current AI systems without slowing down the pace of development.

“That’s another debate — of should we slow down or not,” he said. “But deciding to put the safeguards and monitoring in, that’s a choice we have. And the only question is, Do we do that before or after there’s some large-scale cyberattacks or bioattacks?”

One bipartisan proposal in Congress would require a “kill switch,” or a way for AI companies to immediately suspend or shut down their AI services if a dangerous situation occurs.

Gates said “it’s not enough to have a kill switch” and renewed his calls for internal monitoring and safeguards.

“I would never want to say I’m against a kill switch,” he added. “But when you’re trying to moderate the bad behavior, you need insight and records of what’s being done. So the kill switch alone would not prevent these tragedies.”

The Gates Foundation and AI

The philanthropist, who now leads the Gates Foundation, also spoke about the group’s work expanding AI access to countries where English isn’t the primary language.

“AI models have gotten very good at recognizing English and about a dozen other mainstream languages. And so as you talk about your health, it’s going to accurately record that and be able to help you. In African languages, the error rate will be about 10 times higher,” he said, adding that errors can be “very dangerous.”

The Gates Foundation, he added, is working with 60 AI companies to expand the reliable languages they offer.

“We have a very concrete timeline to improve the quality, to benchmark and then say, ‘OK, this is good enough. You can do a health interview or an education session in this language,’” he added, saying he hopes that within five years every language that a million or more people speak will be close to the quality of English on these platforms.

Gates said his foundation is also using AI in its own work.

“It’s helping me in the foundation work,” he said. “You know, ‘What’s the latest on malaria? What’s the latest on malnutrition?’ It’s allowing the Gates Foundation, which will spend all its money over the next 19 years — you know, it’s got a bit over $200 billion — that money will be far, far better spent because we’re using AI to guide our work.”

A ‘regrettable’ relationship with Jeffrey Epstein

In the last year, Gates has also faced scrutiny for his ties to the late Jeffrey Epstein, the disgraced financier and convicted sex offender. When the Justice Department released a trove of files related to Epstein at the direction of federal lawmakers, Gates’ name appeared multiple times, including in emails from Epstein suggesting that Gates had extramarital affairs.

The Microsoft co-founder has since called his past relationship with Epstein “foolish” and testified to House lawmakers in June that he did not engage in illegal activity with Epstein.

On Wednesday, Gates accused Epstein of using their friendship to boost his reputation.

“I was told by Jeffrey after I met him that he could help raise billions of dollars for global health — you know, he wouldn’t be paid, he would have no role. That proved to be a complete dead end. So my spending time with him, like many other people, kind of helped his reputation. You know, that’s very regrettable,” he said.Share

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Alexandra Marquez

Alexandra Marquez is a politics reporter for NBC News.

Megan Brand contributed.

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