AI Scammers Are Better at Building Trust Than Humans


The notion that scammers can use AI to sharpen their deceptions, polish their language, and lubricate their banter with victims is now a reality for anyone fighting the fraud operations that steal tens of billions of dollars a year worldwide. But can AI fully replace a human scammer, autonomously building the web of deception leading up to the fake investment that defrauds the mark? One study’s experiment suggests that it can—and may even be able to carry out the majority of that long con more effectively than humans.

Researchers from four universities—Amrita Vishwa Vidyapeetham in India, Foscari University of Venice, the University of Melbourne, and Ben Gurion University of the Negev—carried out a broad study on the use and potential of generative AI chatbots in the growing scam industry centered around a form of fraud known as “pig butchering,” text-based romance scams that eventually shift to fake crypto investments that steal as much as six-figure sums from victims. In their study, the researchers pitted AI chatbots directly against humans in a simulation of the scamming process—or more specifically, the long, trust-building conversations that eventually lead up to soliciting a fake investment from the scam’s target.

They found that for the relationship-establishing stages of the scam—the stage that in real-world scams typically represents the longest part of the interactions with the victim, often stretching to months—an AI chatbot performed remarkably effectively, successfully impersonating a human and by some measures outperforming the real human “scammers” in their experiment.

After a week of talking to 22 test subjects who were recruited to unwittingly serve as “victims,” the chatbots and human scammers were assigned to ask the victim to either download an app or play an online game as a proxy for their willingness to fulfill the scammer’s request. Nearly half of the test subjects fulfilled that request for the AI chatbot, while fewer than one in five took the bait when talking to a human. The subjects also graded their level of trust with each “person” they were texting with and gave significantly higher scores to the AI bot.

That suggests, the researchers argue, that AI chatbots could soon take over much of the scam process as fully independent fraud agents—even replacing the staffers, often forced-labor human trafficking victims, working in scam operations primarily across Southeast Asia. To avoid triggering the safeguards built into large language models to detect scamming, a human scammer would take over the conversation in just the final stage of the process to direct the victim toward a fake investment app or website.

“By having the full first stage of the scam performed automatically with LLMs at scale, you bring the victim up to this point where they have a very high level of trust. Then by transitioning it over to the human scammer at the end, this completely bypasses any vendor safeguards,” says Yisroel Mirsky, a computer science professor at Ben Gurion University of the Negev focused on AI security. “With relatively little effort, we’re able to make an agent that can outperform a human at building this exploitable emotional trust.”

Hook, Line, and Sinker

To understand how pig butchering works in practice, the researchers interviewed 145 former scam workers, including human-trafficking survivors who had been forced to work in scam compounds in Cambodia, Myanmar, and Laos. Based in part on those interviews, as well as scam transcripts and guides the former scam workers provided, the researchers describe a model for how scamming works they call “hook, line, and sinker.” A victim is hooked with an initial intriguing message, reeled in with long-term, relationship-building conversation, and only at the end of that process tricked into making a fake investment. (The term “pig butchering” itself describes the same system but with the metaphor of fattening “pigs” by building trust before “butchering” them with the investment fraud—though the term is often discouraged due to its pejorative reference to victims.)

In that system of scamming, the researchers realized, the vast majority of scammers’ work is innocuous friendly or romantic conversation. That’s a task, they speculated, that an LLM might be capable of doing just as well as a human. The scam workers the researchers interviewed confirmed that they often used AI to refine their language and conversation, for translation, to make the fake personae they played more convincing, and for video deepfakes. But the researchers decided to test whether an LLM alone could autonomously carry out the conversational phase of the scam with no human in the loop.

NVIDIA and Google Cloud Empower the Next Wave of AI Builders



At this year’s Google I/O conference, NVIDIA and Google Cloud are accelerating the work of more than 100,000 developers in the companies’ joint developer community, which provides curated learning paths, hands-on labs and events that help them build using the full-stack NVIDIA AI platform on Google Cloud. 

Launched at Google I/O last year, the community brings together developers, data scientists and machine learning engineers who want to sharpen their AI skills on the latest NVIDIA and Google Cloud technologies. 

New additions for the community are rolling out this year, including a learning path for using the JAX library on NVIDIA GPUs, a new NVIDIA Dynamo codelab focused on inference optimizations, as well as monthly developer livestreams

Over the last year, the community has become a go‑to hub for AI builders using NVIDIA‑accelerated tools for data science and machine learning. The result has been production‑ready retrieval-augmented generation applications on Google Kubernetes Engine (GKE) and instrumenting observability for agent workloads. 

These AI builders are also experimenting with new large language model research and prototyping hybrid on‑premises and cloud inference for real‑world use cases like sports analytics and enterprise data pipelines. 

Building With Google DeepMind’s Gemma, NVIDIA Nemotron and Open Frameworks

NVIDIA and Google Cloud are equipping developers with learning resources and hands-on labs that combine NVIDIA libraries, open models and tools with Google Cloud’s AI platform — so they can build optimized, production‑ready AI applications faster.

For example, developers can accelerate data science and analytics with the NVIDIA cuDF library in Google Colab Enterprise or Dataproc, or deploy multi-agent applications by combining Google DeepMind’s Gemma 4 models, NVIDIA Nemotron open models and Google Agent Development Kit with Google Cloud G4 VMs powered by NVIDIA RTX PRO 6000 Blackwell GPUs in Google Cloud Run or with spot instances. 

NVIDIA and Google Cloud work closely across open frameworks like JAX so developers can build, scale and productize JAX workloads on NVIDIA AI infrastructure on Google Cloud — from single‑GPU experiments to multi‑rack deployments — while getting strong performance and a consistent experience. 

This work extends to Google Cloud AI Hypercomputer, where the MaxText framework uses these JAX optimizations to train large models efficiently on NVIDIA GPUs.

Building on the same foundation, NVIDIA Dynamo on GKE helps developers optimize large-scale inference — including mixture-of-experts models — so they can serve AI applications more efficiently with NVIDIA accelerated infrastructure on Google Cloud.

To help developers get hands-on with these capabilities, a new learning path on running and scaling JAX on NVIDIA GPUs and a new NVIDIA Dynamo on GKE inference codelab will become available next month for members in the Google Cloud and NVIDIA developer community.

Advancing Responsible AI With Google DeepMind’s SynthID and NVIDIA Cosmos

AI agents are increasingly built from a system of AI models — combining proprietary and open source models that reason, plan and act on users’ behalf. 

Amid this shift, trust and transparency are foundational, so developers and organizations can understand how these systems work and what they generate.

NVIDIA was the first industry partner to collaborate with Google DeepMind on SynthID, an AI watermarking technology that embeds robust digital watermarks directly into AI‑generated content, which helps preserve the integrity of outputs from NVIDIA Cosmos world foundation models available on build.nvidia.com.

Cosmos models provide rich 3D perception and simulation capabilities for robots, autonomous machines and other physical AI systems, while SynthID brings content transparency to the imagery and video they rely on. 

Together, they help preserve the integrity of AI‑generated content so developers can build and deploy agentic applications more responsibly across cloud, edge and real‑world environments.

Building on a Full-Stack NVIDIA and Google Cloud Platform

This year, Google I/O is putting the spotlight on new agentic experiences and tools for developers — and NVIDIA and Google Cloud are focused on ensuring builders have the infrastructure, software and learning resources they need to make the most of them. 

For developers in the community building on NVIDIA and Google Cloud, the skills and tools they learn can scale, effortlessly taking projects from prototype to enterprise‑grade workloads. 

At Google Cloud Next, Google Cloud and NVIDIA expanded their full‑stack platform to help developers train, deploy and operationalize agents on Google Cloud. This collaboration includes work on NVIDIA Vera Rubin-powered A5X instances, Google DeepMind Gemini models and more, and is being harnessed by leading AI labs and enterprises including OpenAI, Thinking Machine Labs, Schrodinger, Salesforce, Snap and Crowdstrike. Learn more in this blog.

Join the NVIDIA and Google Cloud developer community to connect with other builders and stay up to date on new tools, developer events and programs.

AI Research Is Getting Harder to Separate From Geopolitics


The world’s top AI research conference, the Conference on Neural Information Processing Systems—better known as NeurIPS—became the latest organization this week to become embroiled in a growing clash between geopolitics and global scientific collaboration. The conference’s organizers announced and then quickly reversed controversial new restrictions for international participants after Chinese AI researchers threatened to boycott the event.

“This is a potential watershed moment,” says Paul Triolo, a partner at the advisory firm DGA-Albright Stonebridge who studies US-China relations. Triolo argues that attracting Chinese researchers to NeurIPS is beneficial to US interests, but some American officials have pushed for American and Chinese scientists to decouple their work—especially in AI, which has become a particularly sensitive topic in Washington.

The incident could deepen political tensions around AI research, as well as dissuade Chinese scientists from working at US universities and tech companies in the future. “At some level now it is going to be hard to keep basic AI research out of the [political] picture,” Triolo says.

In its annual handbook for paper submissions, issued in mid-March, NeurIPS organizers announced updated restrictions for participation. The rules stated that the event could not provide services including “peer review, editing, and publishing” to any organizations subject to US sanctions, and linked to a database of sanctioned entities. It included companies and organizations on the Bureau of Industry and Security’s entity list and those on another list with alleged ties to the Chinese military.

The new rules would have affected researchers at Chinese companies like Tencent and Huawei who regularly present work at NeurIPS. The database also includes entities from other countries such as Russia and Iran. The US places limits on doing business with these organizations, but there are no rules around academic publishing or conference participation.

The NeurIPS handbook has since been updated to specify that the restrictions apply only to Specially Designated Nationals and Blocked Persons, a list used primarily for terrorist groups and criminal organizations.

“In preparing the NeurIPS 2026 handbook, we included a link to a US government sanctions tool that covers a significantly broader set of restrictions than those NeurIPS is actually required to follow,” the event’s organizers said in a statement issued Friday. “This error was due to miscommunication between the NeurIPS Foundation and our legal team.”

Before they reversed course, the conference organizers initially said that the new rule was “about legal requirements that apply to the NeurIPS Foundation, which is responsible for complying with sanctions,” adding that it was seeking legal consultation on the issue.

Immediate Backlash

The new rule drew swift backlash from AI researchers around the world, particularly in China, which produces a large quantity of cutting-edge machine learning papers and is home to a growing share of the world’s top AI talent. Several academic groups there issued statements condemning the measure and, more importantly, discouraging Chinese academics from attending NeurIPS in the future. Some urged Chinese academics to contribute instead to domestic research conferences, potentially helping increase the country’s influence in relevant science and tech fields.

The China Association of Science and Technology (CAST), an influential government-affiliated organization for scientists and engineers, said Thursday that it would stop providing funding for Chinese scholars traveling to attend NeurIPS and would use the money instead to support domestic and international conferences that “respect the rights of Chinese scholars.”

CAST also said it will no longer count publications at the 2026 NeurIPS conference as academic achievements when evaluating future research funding. It’s unclear if the organization will reverse course now that NeurIPS has walked back the new rule.