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Misbehaving chatbots could be kept in check with personality tests

Artificial intelligence chatbots need to work on their social judgment, recent events suggest. At one end of the spectrum, they’re facing lawsuits for recommending dangerous actions. At the other end, the models can be so nice they’re considered sycophantic.

The problem could get worse as AI bots work more with humans, such as handling customer complaints, says Yan Leng, assistant professor of information, risk, and operations management at the McCombs School of Business at The University of Texas at Austin.

But help may be on the way. In new research, Leng has devised a sort of personality test—more precisely, a behavioral audit —for large language models (LLMs), the technology that drives products such as ChatGPT. The paper is published in the journal Information Systems Research.

Ann Cavoukian: We have to protect privacy globally or we protect it nowhere!

I recorded this interview 13 years ago.

It should feel dated by now. It doesn’t. It feels like a prophecy.

Back in 2013, Dr. Ann Cavoukian sat down with me as the Information and Privacy Commissioner of Ontario and the mind behind Privacy by Design. She told me privacy was not dead. She told me security and freedom were not a trade-off. She told me metadata reveals more about you than the content ever could.

Then she said something I have never been able to shake:

“We have to protect privacy globally, or we protect it nowhere.”

Think about where we are now. Surveillance is the business model. Your data trains systems you will never see. The “nothing to hide” crowd got louder, and the borders she warned about got thinner. She saw all of it coming.

GreyVibe hackers use ChatGPT, Gemini to power cyberattacks

A likely Russian threat group tracked as GreyVibe has been using AI-generated lures and a rich set of custom malware tools to target entities in the military, government, civilian, and business sectors.

The cyberespionage campaign has been active since at least August 2025 and appears to align with Russian state interests, although researchers cannot confidently classify it as a nation-state operation.

Cybersecurity company WithSecure discovered the activity in January this year and determined that its focus is on Ukrainian or Ukraine-related organizations.

Stelarc on Transhumanism: We Are in a Time of Circulating Flesh!

“We are in a time of circulating flesh.”

Stelarc said that to me 13 years ago. In 2026, it reads less like art criticism and more like a status report.

He had grown an ear on his arm. He had hung himself from hooks 25 times. He had let strangers on the internet choreograph his muscles through electrical stimulation, his body remote-controlled across continents.

Most people called it spectacle. I think it was inquiry.

Because long before deepfakes, before voice cloning, before AI agents wearing our faces, was already asking the question we now cannot avoid:

Where does the body end and the network begin?

Behold the neuron, a complicated cell with a simple mission

Neurons, the uber-connected nerve cells that act as a main switchboard for the brain, are central to some incredibly complicated processes. They make it possible to think, walk, speak, and breathe. They even have built-in backup batteries to use in emergencies.

Yet the way individual neurons go about their business is surprisingly simple, according to a new Yale study.

How simple? Most of them operate entirely like tiny on-off switches.

The Commoditization of Intelligence: Why AI Aggregators Will Beat Foundation Models

Everyone is currently watching the major tech giants throw billions of dollars at the AI arms race, cheering for whichever foundation model happens to top the leaderboards this week.

It is an incredible spectacle to watch unfold, but focusing too closely on the tech itself might mean we are missing the actual business revolution happening right under our noses.

We have seen this exact economic shift before. The biggest winners of the internet era weren’t the ones who built the physical infrastructure or supplied the goods; they were the platforms that organized the supply and owned the user relationship. The same economic laws are now coming for artificial intelligence, actively turning “intelligence” into a basic, interchangeable utility.

The real value moving forward is no longer in the models themselves, but in the seamless interfaces that aggregate them. If you want to protect your business from vendor lock-in and position your team for ultimate flexibility, it is time to rethink your approach.

Read my full blog post to dive into why the future of AI belongs to the aggregators, and how your business can strategically capitalize on this shift.


We spend an enormous amount of time obsessing over the titans of the AI arms race. Every single week seems to bring a breathless new headline about OpenAI, Google, Anthropic, or Meta releasing a foundation model that edges out the competition on some obscure benchmark test. We find ourselves endlessly arguing over parameter counts, context windows, and raw reasoning capabilities, captivated by a multi-billion-dollar war unfolding in real-time.

Deep Dive: The Agentic AI Economy

The Moat: The moat is no longer how smart your AI is; it’s what your AI is allowed to touch. An agent that has “Write Access” to a company’s internal financial system or a medical record database is 100x more valuable than a “smart” chatbot that can only read public websites. Connectivity is the new Intellectual Property.

In the agentic economy, the most valuable human skill isn’t “coding” or “writing”—it is Agentic Orchestration.

The agentic economy thrives on Data Flywheels. As an agent performs a task (e.g., “Review this legal contract”), it gets human feedback (“This clause was too aggressive”). That feedback isn’t just a correction; it’s training data that makes the agent more valuable for the next task. This creates a winner-take-all dynamic for whoever has the most active agents in a specific niche.

We are moving toward an outcome-based economy. However, the real “gold rush” isn’t in building the smartest AI; it’s in building the safest and most connected AI—the one that humans trust enough to give the “keys” to their bank accounts, their calendars, and their businesses.

S02E05 Design Thinking: Enhancing Human Outcomes | Trey Simmons | Wired For Wonder

In this conversation, Lori Kirkland and Trey Simmons from Tamr, explore the transformative impact of AI on the workplace, emphasizing the importance of human-centric design and creativity. They discuss the acceleration of creative processes, the need for prioritization in business, and the shift in mindset required for organizations to thrive in an AI-driven world. The dialogue highlights the significance of collaboration, fluid thinking, and the evolving nature of business assumptions as key elements for future success. In this engaging conversation, Lori Kirkland and Trey Simmons explore essential human skills for the future, the mindset organizations need to adopt for the evolving workplace, and the profound nature of consciousness and reality. They discuss the impact of AI on our understanding of existence and the importance of fostering a culture of love and support in professional environments. The dialogue emphasizes the interconnectedness of humanity and the need for a shift in perspective to embrace change and innovation.

Connect with Trey via / treysimmons.

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You can put a data center at your house—but who really pays?

“The idea, put forward by a California smart utility box company called Span, is to put the GPUs where the power has already been allocated—at the home. Span says the average household uses only about 42% of the electricity allotted to it, and rarely reaches peak usage. Span’s smart utility boxes detect that, and steer the extra available power over to the GPUs, which live inside a ”node” that sits beside the house and looks something like an HVAC unit. The boxes contain 16 Nvidia GPUs, 4 AMD CPUs, 4 terabytes of memory, and a cooling system. When a large number of homes have these, the servers could be connected together in a network and work together on distributed computing jobs (workloads), Span says.

In exchange for hosting a node, Span pays a big chunk of the homeowner’s electricity and broadband internet bills.

And there may even be advantages for putting the compute power closer to the end users that are using the chatbots or AI services, Span says.

It’s a cool idea on paper, but it’s almost completely unproven in real-world use. Span has been prototyping the units but has yet to install any of them beside real homes. I asked Span VP Chris Lander if his company has done technical studies showing that its brand of distributed computing will be fast and robust enough to handle real AI workloads. ‘We’ve done a bunch of technical studies internally [and] a bunch of modeling for different kinds of workloads, both from the business point of view [and] the product point of view and from the technical architecture point of view,’ he replies.


The idea of asking homeowners to host boxes full of GPUs is a symptom of the woeful dearth of data center space needed for AI computing.

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