At IO-AI Tech, a startup about 45 minutes north of downtown Shenzhen, China, I glimpsed a wacky new frontier of blue-collar work. Workers wearing the company’s VR headsets, handheld controllers, and motion-tracking gear remotely control humanoid robots for workplaces like factory floors and convenience stores. The company wants the robots to do useful work, like stocking shelves and picking items out of bins, but it also wants to gather training data that could one day let the bots operate autonomously.

To show off the tech, the company invited me to its offices, where I was allowed to control 10 humanoid robotic hands, each from a different company, using a custom motion-tracking glove. The device instantly transferred my finger movements to all 50 robotic digits.

I’m a little embarrassed to say that the first thing I tried with this futuristic gear was getting all 10 hands to flip the bird. After getting this out of my system, I was impressed by how quickly my movements transferred to the robot hands, and how easily the tech went both ways—I was able to feel a ball placed in one of the electronic hands.

The company also let me try a system that’s being tested by a Chinese convenience store chain. Using a VR headset and a pair of grippers, I tried picking up boxes of medication from a shelf. It was disorienting at first: I had to adjust to a slight difference between my movements and those of the robot I could see through the headset. After a little practice, however, I was stacking shelves like a robot-boss.

Elsewhere, I watched people wearing virtual reality headsets and body-tracking sensors reminiscent of Ready Player One. In one large room, I saw workers using a range of different systems to control diminutive Unitree humanoids. One person marched around with a Unitree robot next to them, and the machine mirrored their movements within a mocked-up apartment. The human operator, wearing a headset and viewing the scene through the robot’s eye-level cameras, went through the motions needed to remove a shirt from a hanger and fold it.

IO-AI develops technology that transfers a person’s movements to different robot forms—a useful offering because there are dozens of different humanoids and robot hands on the market in China today. The startup’s algorithms also need to combine human control with some level of autonomy because a person and a robot aren’t always going to be the same shape, size, and weight. Without some ability to move independently, the robot may lose its balance.

The Trump administration’s disagreement with Anthropic over its most advanced AI models appears to be fast coming to a head.

Trump officials tell Inner Loop that if Anthropic wants to rerelease Claude Fable 5, the AI model that they took offline with export controls last week over concerns about jailbreaking—a method of using prompts to get around a model’s safeguards—the company will need to take steps to actually address what the government alleges are vulnerabilities.

Anthropic has said for days that the administration’s concerns are overblown and that the effects of the jailbreaks are minimal. It reiterated this position to the Commerce Department and the Office of the National Cyber Director, Sean Cairncross, in a technical meeting on Monday.

But officials say they are past arguing whether the jailbreaks are significant, since the National Security Agency concluded that there are ways to disable guardrails on Fable 5, which are put in place to prevent users from accessing capabilities of the Mythos model related to cybersecurity, chemistry, and biology

At this stage, the administration essentially views the situation as Anthropic’s problem to fix, according to three people familiar with discussions.

Neither the Commerce Department’s Center for AI Standards and Innovation nor the National Security Agency has the staff or the bandwidth to be drawn into chasing down every conceivable jailbreak on every model that reaches the market, the people said.

As a result, the administration believes that Anthropic should be more proactive about continually testing not just Fable 5 but all of its frontier AI models to find potential jailbreaks and flag them to the government themselves.

But on a more fundamental level, it remains unclear how Anthropic is supposed to prevent jailbreaking.

Independent cybersecurity experts have increasingly taken the view that guardrails on AI models are only a stopgap solution, since skilled users and future AI models will find ways to bypass constraints—meaning that what the White House appears to want cannot be done.

A White House spokesperson declined to comment.

DNI = Do Not Invite

At the start of the week, Trump’s pick to serve as Acting Director of National Intelligence, Bill Pulte, was on track to never even start the job. Now, Trump has thrown him a lifeline—and it’s the permanent DNI nominee, Jay Clayton, who now faces the prospect of never serving in the role.

To recap: Trump initially named Pulte, his housing finance chief, to replace outgoing DNI Tulsi Gabbard.

Faced with bipartisan pushback because Pulte doesn’t have the national security experience required by law for the role and because he flagged allegedly questionable mortgage fraud accusations against Trump’s political enemies, Trump announced Clayton, the US attorney for the Southern District of New York, as his nominee for a permanent DNI.

Gabbard was scheduled to depart June 18, with Pulte’s first day set for June 19. But Senate Republicans wondered, if Clayton could have his hearing fast-tracked to June 17 and start by June 22, would Pulte even get into the building?

On Wednesday, Trump blew up the plan. As part of a wider feud with Senate Republican leadership over the filibuster, Trump announced Clayton’s hearing would be delayed indefinitely, in an apparent effort to prevent Pulte from getting jumped. Senate Republicans then announced that the hearing would proceed, unless Clayton didn’t appear or his nomination was withdrawn.

The situation may be a body blow for the Office of the Director of National Intelligence, which Trump has directed Pulte to vastly downsize, and staffers have been unimpressed by what they see as Pulte’s minimal effort to get to know the agency and lack of regular briefings, people familiar with the matter said.

Schools are racing to write AI policies, but what if the policy is not the first step? This week, we hear from Aleta Margolis, founder and president of the Center for Inspired Teaching, who argues that real progress starts with a conversation, not a rule. Then EdSurge editor-in-chief Sarah McKibben brings it home with what AI actually looks like at her kitchen table, with two middle schoolers navigating it in real time.

What You’ll Learn:

• A new RAND American Youth Panel survey found that only about one in three students say their school has a school-wide AI policy, and Aleta Margolis of the Center for Inspired Teaching explains why co-creating guidelines with students leads to better outcomes than top-down rule-making.

• A recent NPR and Ipsos poll found that 54 percent of teachers say AI is making it harder for students to learn critical thinking skills, and nearly three in four believe its impact on education will exceed that of the internet or computers.

• Sarah McKibben describes the mix of productive and concerning AI use she sees with her own children, including a student using an AI humanizer app to avoid plagiarism detection when submitting AI-written essays.

• Both guests converge on the idea of productive struggle: the concern is not AI itself but whether students are learning to think with it rather than bypassing the thinking entirely.

Listen here:

If you have ever felt like your TikTok feed is mostly fake content, you are not imagining it. A new report from Kapwing found that 59% of videos served to a brand-new TikTok account are AI slop. That is roughly three times the rate Kapwing found when it ran the same test on YouTube.

How bad is TikTok’s AI slop problem compared to YouTube?

Kapwing built a fresh account on both platforms and manually checked the first 500 videos served to each one. On TikTok, 294 of those videos were AI-generated. On YouTube, only 104 of the first 500 Shorts qualified as slop, putting that platform’s rate at 21%.

The scale of the problem is staggering when you consider that TikTok had already labeled 1.3 billion videos as AI-generated by November. Kapwing also manually reviewed over 10,000 TikTok videos across 20 different content categories to get a fuller picture of where slop tends to cluster.

Which TikTok categories are flooded with AI slop

Kids’ content topped every category, with 57% of the 2,000 videos turning out to be AI-generated. The worst single tag was #cartoonkids, where 97 out of 100 featured videos were artificial.

Science and Education, Health, and History followed close behind, each landing between 33% and 35% AI slop. These are categories where animation and voiceover narration tend to replace real demonstration.

On the other end, Fashion, Music, and Fitness were nearly untouched, each sitting below 2%, likely because those formats rely heavily on real, on-camera presence.

Even though TikTok has rolled out tools for users to dial back AI content in their feeds, this study suggests that what shows up by default still leans heavily towards AI. For now, the burden of filtering slop from substance largely falls on the viewer.

The problem with letting an AI agent loose inside a company is not that it might forget who it is. It is that it has no reason to hold back.

A human employee is restrained by the fear of being fired. An agent, as one investor in Arcade.dev put it, “will exhaustively exploit every permission it inherits” to reach its goal. Arcade has raised $60mn to make sure that, by design, it cannot.

The Series A was led by SYN Ventures, with strategic cheques from Morgan Stanley and Wipro. Added to a $12mn seed last year, it brings the San Francisco startup to $72mn in total funding.

Identity is easy. Authorisation is the wall

Most companies can already verify that an agent is what it claims to be. What they cannot do, according to Arcade chief executive Alex Salazar, is prove that a given agent, acting for a given user, is allowed to perform a given action on a given system.

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“Agents don’t fail in production because the model is wrong,” Salazar said. “They fail because nobody can prove” who is authorised to do what. That gap, he argues, is why so many corporate agents never leave the pilot stage.

Salazar, a former Okta product leader who once sold a startup to the identity firm, built Arcade with chief technology officer Sam Partee, formerly of Redis.

The accidental product

Arcade did not set out to build this. Its first product was an agent that diagnosed misbehaving servers and databases, which required sweeping super-user access. “No one in their right mind was going to actually let us do that in the real world,” Salazar said.

So the team split the model’s reasoning from the layer that actually touches tools, and built the part that decides which tools the agent may use. Nobody was excited about the diagnostic agent. Everybody who understood AI was excited about the authorisation layer. Arcade dropped the agent and kept the plumbing.

Plumbing for the agent era

That plumbing now hangs off Anthropic’s Model Context Protocol, the emerging standard for connecting models to tools like email and internal APIs, to which Arcade says it has contributed. Its runtime checks each request against an organisation’s real permissions, can run inside a customer’s own environment, and logs every action so a company can tell an agent’s move apart from a human’s.

Salazar’s argument for why a control layer has to sit outside the agent is the oldest one in enterprise risk: the thing taking an action never gets to authorise itself. Traders don’t approve their own trades. A smarter model, he says, doesn’t change that, and because most companies run several models at once, the control layer has to be neutral to all of them rather than owned by any one vendor.

It lands amid a rush of startups selling ways to put AI agents to work and, increasingly, to fence them in. Arcade frames the incumbents as solving the wrong problem, with API gateways routing traffic and identity tools proving who you are, when the question is what an agent may do, on which system, right now. Its bet is that the boring layer underneath is where the durable business sits.

The catch

For now this is a roughly 40-person company that still has to scale and defend its turf in a field filling up fast. Several of its headline proof points, production use at the world’s largest banks, a 25-fold jump in usage, thousands of prebuilt tools, are Arcade’s own figures rather than independently verified.

The underlying argument, though, is hard to dismiss. As agents start acting on systems no single person fully understands, the question of what they are permitted to touch stops being a policy document and becomes infrastructure. Arcade is betting it owns that infrastructure.

systems

AI drug discovery is so last year, even though it hasn’t accomplished much yet

In order to move more semiconductor manufacturing onshore, the US needs to depend less on foreign-sourced materials. Now, the government is giving an Alphabet spinoff $500 million in CHIPS Act funds to find domestic minerals, molecules, and chemicals needed for this process.

SandboxAQ (that’s AI and Quantum, for those wondering), which spun off from Alphabet in 2022 under the chairmanship of former Google CEO Eric Schmidt, announced the award Wednesday. The company won’t be doing any manufacturing – this is just an R&D grant to turn the startup’s AI simulation software toward discoveries necessary to build a domestic chip industry.

According to SandboxAQ, the $500 million awarded to it by the Department of Commerce will go toward developing “novel molecules and formulations for semiconductor manufacturing,” including chip production materials that are free of PFAS (“forever chemicals”), new semiconductor fabrication catalysts, magnets that don’t rely on foreign-sourced neodymium and other rare earths, and fab-powering batteries that don’t rely on majority foreign-sourced materials like lithium.

The CHIPS and Science Act, signed into law by President Biden in 2022, was designed in part to dole out $52 billion to US firms to reignite domestic semiconductor manufacturing, which has mostly fled the country for more favorable production environments overseas. Four years on, the government’s many investments have seen some payoff, like the acquisition of a 10 percent stake in Intel to help keep the company afloat, but there’s still a lot of work to be done to reduce dependence on foreign supply chains and manufacturers. 

SandboxAQ relies its own large quantitative models (LQMs), which it describes as “AI systems trained on the laws of physics, chemistry, and biology, not human language.” That, the company asserts, means they’re well-suited to discover new materials needed to eliminate harmful PFAS and foreign-sourced materials from the semiconductor supply chain. 

The hope is that the LQMs will be able to generate their own material predictions that researchers then test in the lab – essentially the same process that’s undergirded the years-long effort to use AI to help synthesize new drugs. 

Despite AI industry leaders prognosticating we’d be popping AI-designed drugs in 2025, AI has yet to design a functional medicine, according to the US National Institutes of Health. Why, then, should we presume an AI will succeed at replacing critical battery and chip manufacturing components where drug research has failed?

In fact, according to SandboxAQ’s announcement, its LQMs aren’t even necessarily grounded in real-world data. They rely in part on synthetic data, which is then fed into the company’s LQMs and used to train their design-make-test workflows.

A company spokesperson told The Register in an email that it still uses real-world data where possible.

“Where experimental data exists, we incorporate it,” SandboxAQ told us. “Where it doesn’t, we can still move forward and solve the problem.”

When asked whether an error in the reasoning process could compound, leading to considerable lost time for researchers and a lack of results, the company admitted that such a potential is exactly what “any rigorous AI-driven materials program has to answer.” 

“Our models are trained on the laws of physics and chemistry, so they are anchored to physical reality, rather than free to drift,” the spokesperson told us, adding that lab testing is the final check on AI accuracy. “A material either performs in the lab, or it doesn’t, and that validation gate is precisely what prevents a chain of reasoning from running away with itself.” 

SandboxAQ added that it is not starting from zero in any of the four target areas, having done previous work on catalysts, battery materials, alloy discovery, and PFAS breakdown that will be incorporated into its CHIPS Act-funded work. 

“In commercial deployment, we’ve already cut development timelines from months to weeks” at the candidate screening stage, the SandboxAQ spokesperson explained. SandboxAQ said that some of the work it’s doing, like PFAS mitigation, could be rolled out to existing fabs, as could new batteries and the like, but it admitted that the various verticals will operate on different timelines.

“Qualification in the semiconductor industry is genuinely rigorous and does take time – we wouldn’t minimize that – but the path runs through validation and industrial qualification with existing manufacturers, not through standing up new fabrication capacity from scratch,” SandboxAQ told us. ®

Knowing absolutely nothing about you other than the fact that you’re currently reading Hackaday, I can predict with a high degree of certainty that we’re both fond of at least a few of the same movies. That’s not to say they’re necessarily our favorite works of art. Indeed, in some cases they may even be objectively bad films. But the memory of them has stuck with us — and by extension nearly everyone else in the hacker and maker community — for decades.

Even if you don’t remember all the little details, you’ll never forget the names: movies like WarGames, Ghostbusters, Back to the Future, and Short Circuit. Stories that showed smart people using their intellect and a bit of cobbled together hardware to triumph over the bad guys. The tech wasn’t always believable, sometimes it was downright farcical. But they made it seem real, and by the end of the story when they won the day using brains and a soldering iron rather than fists or a gun, the minutia of how it all worked wasn’t really that important anyway.

It’s not a stretch to say that films such as these helped put many of us on a path towards science and technology. For those with an interest in more cerebral pursuits, seeing a scientist or an engineer save the day was hugely influential. How many engineers got their start watching Scotty frantically eke just a bit more power out of the Enterprise?

But as we recently discussed some of these classic movies behind the scenes here at Hackaday, it struck us that all of the best examples we could come up with were now 20, 30, or even 40 years old. That’s not to say there aren’t a few contemporary standouts, but they mostly seem to be biopics or other historical dramatizations which don’t quite scratch the same itch. Even so, none of them appear to have had the cultural impact necessary to stand the test of time in the same way their predecessors have.

So where have all of Hollywood’s heroic nerds gone, and what does it mean for future generations if these niche role models are no longer represented?

Evil Geniuses and Thick Glasses

Before we get lost down memory lane, we should acknowledge that there’s undoubtedly an element of survivorship bias at play here. We naturally identify with the examples that put techie types on a pedestal, and tend to forget about the less flattering portrayals. In truth, it seems that there’s was only a short period of time in which the classic “nerd” characters got promoted from comedic sidekick roles to protagonists. Before that, and arguably after, it’s a different story.

In the early days, the archetype of the “Mad Scientist” was extremely pervasive. From the 1940s up until the 60s or so, you’d be hard pressed to find a drive-in that wasn’t showing the latest hideous creature pieced together by an unscrupulous doctor. But it wasn’t a concept limited to horror and science fiction. After all, MI6 wasn’t in the habit of dispatching James Bond to defeat drooling imbeciles. Whether they knew how to build killer robots or were titans of industry, the smartest person in the room was often seen as the most dangerous.

In a way, that was still less insulting than the alternative. If a scientist wasn’t trying to forcibly transplant somebody’s brain, they probably had a pocket protector, horn-rimmed glasses, unkempt hair, and buck teeth. My sincere apologies to any readers who may currently meet that description. They might not have been the “bad guy” in the traditional sense, and may even have ended up helping out the heroes in their own way, but nobody was looking at the screen and wishing they were the one with the lisp and the lab coat.

A particularly notable case is The Nutty Professor, in which Jerry Lewis portrays the quintessential nerd who uses his knowledge of chemistry to create a confident and suave alter-ego for himself in the style of Jekyll and Hyde. To be fair, the movie ultimately makes a statement about being true to yourself and the importance of what’s on the inside. But ironically, more than 60 years later, the imagery of Lewis hamming it up as a socially awkward intellectual is undeniably the film’s most indelible element.

The Era of Golden Geeks

At the dawn of the 80s, things started to change. You still had the classic bespectacled nerd, but increasingly films started to put greater focus on their skills and abilities. The “geeks vs jocks” trope became very popular, perhaps most famously exemplified by the Revenge of the Nerds franchise which managed to wring four films out of the concept.

Now a new breed of nerd started to emerge in film that was young, charismatic, and handsome. The only thing that identified Matthew Broderick’s character in WarGames as anything other than a normal teenager in 1983 was the fact that he had a computer in his bedroom and knew how to program it. Steve Guttenberg played a heartthrob roboticist in Short Circuit, and they really screwed the curve up for the rest of us when they cast Val Kilmer as a laser prodigy in Real Genius. The nerds even started to find love, and one wonders how many young men spent their evenings furiously flipping switches on the front panel of their IMSAI 8080 in hopes that a breathless Ally Sheedy might appear in their doorway with an urgent mission that needed their unique expertise. I don’t know about anyone else, but I still haven’t given up hope.

Even school-age kids were getting in on the action. In 1985, Explorers featured a trio of youngsters who built their own spacecraft after assembling a circuit board based on a schematic they collectively dreamt about. The same year saw the release of The Goonies, and while only one of the kids was a tech wiz, they were all clearly meant to be somewhat off-center socially.

Of course, the most famous and culturally relevant example of 1980s nerds using their tech skills to save the day is Ghostbusters. Three 30-something scientists not only determine the physical properties of supernatural entities through empirical research, but also design and construct the equipment necessary to combat them. The resulting “Proton Pack”, which brilliantly captured the look and feel of a piece of hardware hastily thrown together from scavenged parts, became what is arguably the most iconic prop in cinema history. Not only has it been lovingly and reverently recreated by hackers and makers countless times since the movie’s release in 1985, but not a Halloween goes by that you won’t see at least one strapped to the back of a child.

What’s a Nerd, Anyway?

There’s little question that the 1980s represent the high-water mark for nerds in media, but it’s not as if somebody flipped a switch and it all ended at once. There are a few standouts from the early 1990s, with Sneakers coming immediately to mind. It not only meets all of the criteria we’ve discussed here, it’s legitimately an excellent film with an incredible cast. If you haven’t already, please go watch Sneakers.

But for all the hate it’s gotten over the years, I’d also give the nod to Hackers. With a reminder that technical accuracy was never one of the criteria, it absolutely ticks the proper boxes when it comes to young, competent people using their technical skills for good. Plus, if Kilmer raised the bar for hot hackers in film, Angelina Jolie sent it into orbit.

Although the aesthetic benefit that Jolie’s character brings to the film is beyond contestation, it’s important to note that Hackers presents her as exceptionally skilled, with abilities that meet or exceed those of her male peers. The fact that those abilities are accepted by every character in the film without question is a testament to how the audience’s expectations were changing at the dawn of the 2000s. The boys in Revenge of the Nerds might have been able to get away with a panty raid in 1984, but by 1995, the girls were popping shells with the best of them.

That said, those evolving standards may be the reason these type of movies seem to be so uncommon today. Given the expectations and the technical proficiency of the average moviegoer in 2026, what exactly would a nerd hero actually look like? The nerd stereotypes from the Nutty Professor era would be all but completely unrecognizable to modern audiences, and while one could argue that the likes of Elon Musk and Jeff Bezos are getting uncomfortably close to real-life Bond villains, that’s taking us in the wrong direction.

The reality is, it will take more than a teenager with a computer to captivate audiences today. Or to put it another way, if everyone in the theater is at least a little bit of a nerd to begin with, it’s much more difficult to create that mystique on the screen without taking the story to fantastical lengths.

Or at least, that’s one possibility. We’d love to hear your thoughts on the past, present, and future of nerds in the media. Will we ever see the likes of Real Genius and WarGames again, or has the world simply moved on? Are nerds normal?