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Sometimes, reality can make for as good or better stories than fiction . Film has helped elevate some of those stories to greater dramatic heights, from more personal, high-stakes stories like Christopher Nolan 's Oppenheimer about the weight carried by the man behind the atomic bomb, to sweeping epics like Lawrence of Arabia , based on the life of T.E. Lawrence . Often, the most inspiring of those tales are some of the best , showing what humanity is capable of when they strive to give back, do something no one else has, or work together to achieve the seemingly impossible. Half Crown Media and Burns & Co. Entertainment are now preparing a film that aims to live in the same realm, recounting the arduous efforts of a missionary in China during the height of the Opium Wars in the 19th century.

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Hugging Face’s incident response team first turned to frontier AI models to analyze a breach of the company’s production infrastructure, and the models refused to help. Commercial safety guardrails built to stop attackers blocked every forensic query because they treated the IR team’s real exploit data the same way they would treat a live attack.

The attacker, an autonomous AI agent running the campaign end to end, moved laterally across the Hugging Face infrastructure for a weekend, undetected and unstopped.

Security leaders are quick to recognize the pattern and diagnose what went wrong. “I’ve seen versions of this during red-team exercises and internal security testing, but this is one of the first high-profile examples where it materially affected real incident response,” said Merritt Baer, senior adviser to Andesite, G2I, and AppOmni and former Deputy CISO at AWS.

None of this is unique to Hugging Face, Baer said. “Commercial frontier models optimize for preventing misuse. They generally have no cryptographic or organizational way to determine whether the person asking ‘analyze this malware’ is an incident responder or the malware author.”

A malicious dataset opened two code-execution paths

On July 16, Hugging Face disclosed that an autonomous AI agent system had compromised its production infrastructure, gaining unauthorized access to a limited set of internal datasets and several service credentials. The company verified that its software supply chain was clean and found no evidence of tampering with public models, datasets, or Spaces.

Hugging Face is still assessing whether the intrusion touched any partner or customer data and says it will contact affected parties directly as required. But few enterprise threat models had accounted for the attacker Hugging Face faced. An autonomous agent ran the entire campaign from initial access through credential harvesting. No human guided it.

The entry point was a malicious dataset. When the data-processing pipeline ingested it, the dataset triggered code execution in two ways, through a remote-code loader and through a template-injection flaw in dataset configuration files. No admission gate screened the file before it reached a processing worker, the compute job assigned to each dataset. Initial access came through the data pipeline itself, a route that worked because most enterprise security teams treat the data feeding their pipelines as trusted input rather than an attack surface.

Worker isolation did not hold. From that first foothold, the agent broke out onto the node running the workload, then harvested cloud and cluster credentials scoped broadly enough to reach multiple internal clusters, all of it inside a single weekend. Hugging Face described the agent framework as executing thousands of individual actions through a swarm of short-lived sandboxes, with self-migrating command-and-control staged on public services. The disclosure adds that the framework appeared to be built on an agentic security-research harness, which would put tooling designed for red-team work behind a live intrusion.

Why the defenders’ queries looked like attacks

Investigators reconstructed more than 17,000 recorded events using AI-driven analysis agents of their own.

First attempts at the log analysis ran on frontier models behind commercial APIs. Defenders’ steps included submitting real attack commands, exploit payloads, and command-and-control artifacts for classification, but safety guardrails blocked the requests outright.

Baer traced the block to the prompts themselves. “The same prompts that are most valuable during an active intrusion, shell commands, exploit chains, credential dumps, persistence mechanisms, lateral movement, are exactly the prompts most likely to trigger safety systems,” she told VentureBeat. “As AI becomes embedded in security operations, this becomes an operational resilience issue rather than merely a model policy issue.”

The forensic analysis finished on GLM 5.2

GLM 5.2, an open-weight model deployed on Hugging Face’s own infrastructure, took the job the commercial APIs refused. No attacker data left the company’s environment. “This experience points to a gap worth planning for,” the company wrote in its disclosure. Hugging Face does not know which model powered the agents. It could have been a jailbroken hosted model or an open-weight model running without restrictions. Either way, the disclosure continued, “the attacker was bound by no usage policy, while our own forensic work was blocked by the guardrails of the hosted models we first tried.” Hugging Face drew that line itself, writing that the experience is not an argument against safety measures on hosted models and that it is sharing the feedback with the providers concerned.

What authenticated trust changes

The industry, Baer argued, needs to move past treating AI safety as a content moderation problem. “Security operations require something different. Authenticated trust.” Instead of asking whether anyone should receive an answer, the question becomes whether an authenticated security team, operating under enterprise controls, should receive it. “The model shouldn’t only understand what is being asked. It should understand who is asking, why, and under what governance.”

“Organizations already build contingency plans for cloud outages, identity provider failures, or EDR failures,” Baer wrote. “AI assistants are becoming another dependency.”

Her advice on IR playbooks was blunt. “A mature incident response plan should assume that during a severe incident, commercial AI APIs may refuse requests, API rate limits may become unavailable, internet connectivity may be impaired, and data governance rules may prohibit uploading forensic evidence externally.” The lesson, she wrote in her emailed answers, “isn’t ‘don’t use commercial models.’ It’s ‘don’t make them a single point of failure.’”

AI-enabled attacks rose 89% year-over-year

Autonomous AI-driven attacks are not limited to AI platforms. CrowdStrike’s 2026 Global Threat Report documented AI-enabled adversary operations increasing by 89% year over year, with average breakout times falling to 29 minutes. Enterprises running AI workloads in production with agentic access to their pipelines face similar exposure.

Six control domains determined the blast radius and recovery speed at Hugging Face. Each one maps to a concrete action security leaders can take before the next autonomous-agent breach arrives.

AI Pipeline Breach Response Playbook

Control Domain

What Broke

Monday Action

Dataset admission controls

Two code-execution paths were exploited. No admission gate validated the dataset before it reached a processing worker. The data pipeline became the initial access infrastructure.

Require sandbox execution and static analysis of all datasets before they reach workers. Block remote-code loaders and template-injection paths by default. Audit for any path granting code execution to untrusted content. Report to the board as a supply-chain risk.

Worker-to-node privilege boundaries

Worker isolation failed to prevent escalation to the node. The agent gained cluster credentials because the workload-infrastructure boundary was never enforced at container runtime.

Enforce hard privilege boundaries between workers and nodes. Deploy container runtime security to prevent workload escape. Audit whether workers can reach node-level APIs or credential stores. Include in the next penetration test scope.

Credential exposure

Cloud and cluster credentials harvested after node access. The scope was broad enough for lateral movement across multiple clusters over a weekend.

Rotate credentials on a scheduled cadence and after any anomaly alert. Scope to the minimum cluster and service. Deploy monitoring that flags access from unexpected nodes at machine speed. Map blast radius for board reporting.

Machine-speed detection

Thousands of actions through short-lived sandboxes with self-migrating C2. AI-assisted anomaly detection surfaced the campaign after a weekend of lateral movement, per the disclosure.

Calibrate detection for machine-speed patterns. Ensure high-severity alerts page responders in minutes, regardless of time. Audit SIEM rules for detecting thousands of short-lived executions within a single hour.

Private AI forensic capacity

Commercial APIs blocked forensic analysis. Guardrails screened query content, never analyst identity. Investigation ran on GLM 5.2 privately.

Deploy a capable open-weight model on private infrastructure before an incident. Test against real forensic workflows. Ensure IR playbook includes fallback for when commercial APIs refuse. Document gap for cyber insurance.

Autonomous-agent threat modeling

The campaign matched the forecast agentic-attacker scenario, but no threat model had operationalized it. LLM powering the agent is still unknown.

Add autonomous AI agents as a distinct adversary class with machine-speed decision cycles. Run tabletop at agent speed. Present results to the board as evidence that timelines need recalibration. Include in the cyber insurance application.

The board question is operational resilience

“The question for directors is simple. What happens if one of our critical security tools becomes unavailable during the exact moment we need it most?” Baer framed that as operational resilience, not AI policy.

She would have boards take that framing straight to management and press for specifics. “Have we actually exercised that fallback during tabletop exercises? How quickly can we switch during an incident?” Procurement needs to change alongside governance, starting with the questions buyers ask. Security teams evaluating AI vendors should ask about their process for authenticated incident responders, whether enterprise customers receive different handling during verified incidents, and whether models can be deployed privately. “Those questions belong alongside uptime, privacy, and compliance,” Baer said.

“The biggest takeaway isn’t that safety guardrails are ‘bad.’ They’re doing what they were designed to do,” she argued.

Her larger point is that the threat model itself has changed. “For decades, defenders had better tools than attackers because they operated inside trusted enterprise environments. With foundation models, both sides increasingly use the same capabilities, but one side is constrained by enterprise governance, policy, compliance, and safety controls, while the adversary simply downloads an uncensored open-weight model and keeps going. That’s a new kind of asymmetry,” she added. “The organizations that handle it best won’t necessarily be the ones with the most powerful AI. They’ll be the ones that architect AI as a resilient security capability rather than a single cloud service.”

Hugging Face has contained the intrusion, rebuilt compromised nodes, rotated credentials, and reported the incident to law enforcement. The company recommends that all users rotate access tokens and review recent account activity. Mid-incident, Hugging Face found out whether its own AI tooling would be available, and the first answer was no. Security leaders running AI in production should find out in incident response planning instead, before an autonomous agent forces the test.

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The city of Kyoto has over 1,600 Buddhist temples. While this is a very large number, it often feels like this isn’t enough. Famous sites such as Kinkaku-ji , Kiyomizu-dera , and Ryōan-ji are often seen over the shoulders of hundreds of other visitors, who tend to be cropped out of social media posts. In this environment, it may seem difficult to find a profound experience with this ancient, sacred city. Still, amongst this chaos, there is at least one temple that has done its best to limit rather than encourage visitation.

Saihō-ji is a Rinzai Zen Buddhist temple on the western edge of Kyoto, below the slopes of Arashiyama. The name means “Temple of the West Fragrance” and is dedicated to Amitabha, the Buddha of the Western Pure Land. The temple is also known by another name: Kokedera, the Moss Temple. The garden is drenched in moss, in over 120 varieties. The moss arrived as something of a happy accident during the Meiji era, as the temple lacked funds to maintain the more tidy gardens of the past.

Famed for its moss garden, Kokedera was once one of the most popular and visited temples in Kyoto. However, in 1977, the temple made the decision to end walk-up visits. One would have to reserve their place in advance by postcard. Additionally, one would have to engage in one of the temple’s spiritual activities. For most guests, this takes the form of copying a sutra. The words are faintly written on the paper, so visitors who don’t know Japanese can simply trace the words.

Once the sutra is complete, you may choose to take it home, or you may donate it to the temple for ritual use. Only then can you enter the beautiful moss garden. The many shades of moss are fascinating when viewed up close as tiny forests, and breathtaking in their totality, making the entire forest look like one great, living entity. It is no wonder that this garden is a representation of the Buddhist Pure Land.

As entry has become more exclusive, Kokedera has lost some of its fame. It is generally left off of online itineraries, possibly due to the inconvenience of scheduling a visit. However, for those few who make the trip, it is one of the most enriching experiences they will have in Japan. Whether calmed by the act of copying the sutra, the lovely moss landscape, or a shared reverence for the moment, visitors tend to keep their voices down and quietly enjoy the garden. It is the type of encounter that will leave you with a deep sense of wonder for nature, and perhaps, a little more faith in humanity.

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For three seasons, The Toys That Made Us has uncovered the hidden histories of the most beloved playthings in modern history, from Barbie to Lego to Power Rangers . Now, the behind-the-scenes artwork showcased on the series is going to be collected in a handsome book, The Art of The Toys That Made Us , for which Toys That Made Us producer Nacelle and publisher Oni Press are launching a Kickstarter. The Kickstarter will also include Nacelleverse: Year Zero , the collected edition of Nacelle's all-new comics featuring tales of several cult-classic toy lines. Collider is proud to exclusively reveal the all-new wraparound cover for Nacelleverse: Year Zero , by Canadian comic artist Karl Kerschl ( Gotham Academy ).

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Albert Einstein is one of a handful of scientific minds in history who made profound mental leaps that arguably no other researcher could have achieved during their era. There just wasn’t sufficient scientific data to motivate anyone else to come up with Einstein’s theories of special and general relativity (though some came close ). In fact, the core principles of relativity are counterintuitive to minds that evolved here on Earth, where objects move slowly, gravity feels like a force, and time is a yardstick that consistently measures our lives.

Famously, Einstein made his breakthroughs by performing thought experiments in which he imagined himself in extreme physical situations, like riding a beam of light or standing in a falling elevator. By projecting himself into a reality beyond our natural human experience, he transformed our understanding of space, time, and gravity.

Of course, Einstein isn’t the only innovator to make mental leaps that were well ahead of his time. Alan Turing invented the field of computer science before anyone had built a truly programmable computer. Using pure logic and imagination, he envisioned a “ Universal Turing Machine ” decades before the components existed to create one. He even predicted that programmable machines would enable human-like intelligence, a milestone we now call “artificial general intelligence” (AGI) , and put forth his famous Turing Test to assess when AI reaches this level.

Unfortunately, Turing’s test relies too much on human judgement. He proposed using a human evaluator to hold a blind conversation with both a human and a machine. If the evaluator could not reliably determine which participant was human, the machine would have implicitly matched human intelligence. It turns out, this test fails because people are too easily fooled due to our inherent tendency to anthropomorphize anything that behaves remotely human.

A better Turing Test

Many benchmarks have been proposed for assessing the arrival of AGI , but most require machines to solve problems whose solutions could be embedded within the data used to train them. As a result, it has been challenging to find repeatable tests that require an AI to make genuinely original and creative leaps.

This brings me to the “ Einstein Test ” proposed by Benrimoh et al. and recently endorsed by Nobel Prize-winning computer scientist Demis Hassabis. The concept is to build special versions of frontier models trained only on the scientific information available to Einstein before his breakthroughs. If such an AI system could independently derive the special and general theories of relativity, it would demonstrate that AI could make creative leaps at the upper limits of what a human brain has ever achieved.

I like this test because it sets a high bar and could be replicated in disciplines outside theoretical physics. Hassabis, who oversees Google DeepMind, suggested the Einstein Test is a good benchmark for AGI, which he estimates there’s a 50% chance of achieving by the end of the decade. If this happens, it could unleash a flood of scientific advancements as the world suddenly deploys thousands of “AI-powered Einsteins” to solve thousands of hard problems, all at software speeds.

As context, we have experienced rapid acceleration in innovation over the past 100 years as the world transitioned from mechanical improvements, which progressed at a roughly linear rate, to digital advancements that have progressed closer to an exponential rate. AGI could push things even faster, driving innovation at a hyperbolic rate because AI is likely to operate at steadily increasing speeds and could trigger recursive loops of self-improvement.

Would this be good for humanity?

I’ve been focused on this issue for 20 years. While I agree with Hassabis that society would benefit from a tsunami of genius-level advancements, I fear superintelligence could have a demoralizing impact on us humans. After all, when AI systems can make leaps at the level of an Einstein or Turing, and do so in a fraction of the time, human scientists and inventors may never have the opportunity to make grand breakthroughs. In fact, from an innovation perspective, we could soon pass “ Peak Human ” as AI systems take the reins.

I hope this doesn’t happen, but superintelligence could mean we never produce another genius-level innovator like Einstein or Turing, at least not a human one. In addition, this threat might not be limited to science and technology. What if superintelligence means we never have another human Shakespeare, Aristotle, Confucius, or Dickinson? This is harder to predict, but certainly within the realm of possibility. Would this be demoralizing?

In the spirit of an Einstein thought experiment, ask yourself this: How would you feel if your favorite new TV show were written entirely by AI ? Or your favorite new comedian, a hilarious AI? Or the hottest new fashion designer? Or the most influential political thinker? None of these things are certain to happen, but all could happen. If so, how would that impact our collective psyche? Our human dignity? This is a genuine risk factor that is not getting sufficient attention. In fact, this month, the United Nations held its first-ever Global Dialogue on AI Governance in Geneva, and these issues were not on the agenda.

I know that many who read this will insist AI could never write the next Breaking Bad or do creative standup at the level of George Carlin, and I hope those people are correct. That said, I worry those views are rooted more in denial and wishful thinking than an objective assessment of the power and trajectory of AI. In fact, I see many people cling to outdated views of AI being nothing but statistical parrots. As I covered in Big Think in January , this stance is untrue of modern AI systems like Gemini and Claude.

Believe me, I would love to see AI hit a wall before reaching human-level intelligence. In fact, I clung to this view for much of my career. Then, 15 years ago, I asked myself a simple question: Why is this the most likely future? I had no reasonable answer and had to admit it was rooted mostly in wishful thinking.

So, I asked the next logical question: What can we do to ensure that human intelligence remains relevant in an age of superintelligence? For me, the answer boils down to five simple words: keep humans in the loop . I am not talking about giving an elite group of people supervisory control over a superintelligence in the hope they can contain it or align it with human interests — I’m skeptical either will work over time. I mean building systems in which large numbers of ordinary humans are part of the thinking itself.

Specifically, I believe we should shift our priority from purely digital superintelligence to a hybrid method I refer to as “ collective superintelligence .” This means building systems that incorporate human values, morals, sensibilities and interests into the process — not by training them on data about humans but by connecting them to groups of real humans .

This might sound like science fiction, but evolution has gone down this path many times before. From bird flocks and fish schools to bee swarms, groups of brains can form real-time systems that exceed the intelligence of every individual member by leveraging their combined insights and perspectives. Biologists call this “swarm intelligence,” and as I described in Big Think in June , it could enable humans and AI agents to “think together” at scale, producing superintelligent systems that are genuinely human at their core.

From a practical perspective, instead of releasing millions of AI agents designed to replace people (as is currently happening), we should be releasing AI agents designed to connect people — harnessing, amplifying, and integrating our human collective intelligence with AI systems. The resulting hybrid collective superintelligence would likely drive innovations more slowly than a purely artificial superintelligence, but it would think in ways that are inherently more human than the alternative.

Personally, I’ve been researching the potential of hybrid human-AI systems for the past decade through a company I founded, Unanimous AI. In an early NSF-funded study performed in collaboration with Stanford University Medical School, we found that teams of human doctors working in collaboration with AI systems could outperform both humans and AI systems working in isolation, demonstrating a symbiosis that harnesses the advantages of both while overcoming their respective limitations.

I know that many people reading this would prefer that superintelligence never happens (myself included), for it will mean there are countless brains in our lives that are smarter, faster, more creative, and more knowledgeable than any person — even Einstein-level geniuses. In other words, for the first time since humans climbed down from the trees, our species may lose cognitive supremacy in our natural environment. This changes our role in the world.

Can we prevent it? Unfortunately, the race to AGI is unlikely to slow — the economic and political pressures are only growing stronger. Superintelligence will likely arrive , and it could make us feel like mere observers in a rapidly changing world that’s driven more by AI than humans. An alternative is to push for collective superintelligence . If we take this path, there still could be another human Einstein — it might just be all of us, together.

This article Have we seen the last Einstein? is featured on Big Think .

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GameSir has issued an update to its Connect software, which removes a big point of friction for people who regularly use the app to customize their PC controllers , but the update could also be really helpful if you've never been up to speed on your controller terminology.

The GameSir Connect app is what you'll use to personalize the GameSir G7 Pro 8K PC model as well as a few of the other more recent GameSir products. Until now, we've been forced to uninstall each obsolete version of the application when a new one arrives, before finding the latest one on the Windows Store.

As of now, those days are officially done. Now on version 1.14.4, the application will update the version you already have instead of forcing you to find an entirely new app.

This will definitely go some way to cutting out confusion, especially since, depending on what GameSir controller you have, there are different apps you need to use. The original GameSir G7 Pro for Xbox and PC uses the older GameSir Nexus app, for example. Either way, if you have a compatible controller, the latest version of Connect will allow for less foot-dragging in the Windows Store and more streamlined customization.

I'd guess that most pro controller owners probably use their gamepad's controller software a total of once, on average. Beyond the initial setup when you unbox it, though, companion software can offer you loads of versatility and value for money for whichever gamepad you've opted for. GameSir's apps have always been great, but a streamlined updating system isn't the only improvement the brand has made to Connect.

(Image credit: Future / Duncan Robertson) Perhaps most helpfully, a new information feature has been added that'll explain what some of the settings actually change. So if you've never been sure of your jargon controller terms, all you'll now have to do is hover a cursor over an information symbol next to each section header to get a brief description.

So if you have trouble distinguishing between deadzones, polling rates, hair trigger mode, or more, you can now rest easy knowing that using an in-depth customization app like Connect isn't going to take you out of your depth. I'm sure for some, not knowing these terms is a reason they've avoided using companion apps to begin with.

(Image credit: GameSir) With the latest firmware update for the G7 Pro 8K PC, you can also receive a latency improvement, which GameSir says will be narrowed down to less than 250 milliseconds. Furthermore, if you want a more granular way to modify the settings on your thumbstick deadzone curves, the new version of Connect will allow you to input coordinates of 100x100.

Beyond the aforementioned GameSir controllers, the Connect app is also going to be the settings home for the GameSir Tarantula Pro , Cyclone 2 , Super Nova, Tegenaria Lite, and Nova 2 Lite. Older controllers may still use the GameSir Nexus app.

In the summary video about the software update on GameSir's YouTube channel, the upcoming GameSir Tarantula Pro 8K PC was also mentioned, which likely points to this controller using Connect when it hits the shelves.

GameSir also makes some of the other best Xbox controllers , best Switch 2 controllers , and best mobile controllers.

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Bugatti’s Americas COO avoids driving the brand’s own cars in public.

His daily driver comes from the same group, several rungs further down.

Doering said the Tourbillon reached production without AI touching its design.

Bugatti builds 80 to 100 cars a year for the entire planet, and roughly 40 reach American buyers in a good year. That number shapes how Sascha Doering does his job. The COO of Bugatti Americas knows his clients’ kids’ birthdays, calls them for no particular reason, and asks them to dinner when their travel overlaps. What he doesn’t do is drive a Bugatti home or let AI anywhere near his work.

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Having previously worked at an IKEA cafeteria, Doering slowly worked his way up the corporate ladder to his current role at Bugatti . He maintains close relationships with the company’s clientele and does spend plenty of time behind the wheel of Bugatti models, albeit only alongside customers at rallies and organized driving events

Read: Bugatti Just Built Its Last W16, Unless You’re Rich Enough To Order One

According to Doering, those relationships often extend well beyond the sale. Dinners and phone calls aren’t a sales tactic so much as the job itself. “Personal relationships are of the utmost importance,” he said.

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“I know where my place is,” he told Business Insider in a recent interview. “I don’t want to create a perception that the COO of Bugatti of the Americas is going for a joyride in a Bugatti.”

A Humble Daily Driver

So, what does Doering drive instead? Perhaps unsurprisingly, he parks another VW Group vehicle in his garage, namely an Audi A8 L . While it’s far removed from a Bugatti in terms of performance, it, too, has an obvious focus on quality and luxury. As a means of traveling to meetings and grabbing dinners with owners, something Doering says he frequently does, an executive sedan sounds perfect.

Losing even a single buyer stings at that kind of volume. “We cannot substitute clients. We cannot lose clients because they are so rare, as rare as our products are,” Doering said.

AI? No thanks.

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Then there’s the matter of artificial intelligence . The world is in the middle of an AI boom, and companies that don’t embrace it are at risk of being left behind. Many of the world’s largest car manufacturers are using these tools to help with the development and design of future models. However, Bugatti remains committed to a more traditional approach.

During the same interview, Doering noted that the company didn’t use AI in the design, development, or production of the new Tourbillon hypercar. “They are not utilizing AI for design purposes,” he added. “Maybe that becomes a thought in the future, but at the moment, in the Tourbillon, I don’t think it has been.”

Doering himself isn’t big on AI either, noting he still puts together his own PowerPoints and only occasionally uses Claude for simple, routine tasks. He said preparing presentations himself forces him to work through the analysis and storytelling instead of outsourcing the process. “I’m still one of the guys who writes his own story and puts his own PowerPoint together,” he told the publication.

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Photos Bugatti