AI isn’t new to Hollywood — but this was the year when it really made its presence felt. For years now, the entertainment industry has used different kinds of generative AI products for a variety of post-production processes ranging from de-aging actors to removing green screen backgrounds. In many instances, the technology has been a useful tool for human artists tasked with tedious and painstaking labor that might have otherwise taken them inordinate amounts of time to complete. But in 2025, Hollywood really began warming to the idea of deploying the kind of gen AI that’s really only good for conjuring up text-to-video slop that doesn’t have all that many practical uses in traditional production workflows. Despite all of the money and effort being put into it, there’s yet to be a gen-AI project that has shown why it’s worth all of the hype.
Technology
Hollywood cozied up to AI in 2025 and had nothing good to show for it
This confluence of Hollywood and AI didn’t start out so rosy. Studios were in a prime position to take the companies behind this technology to court because their video generation models had clearly been trained on copyrighted intellectual property. A number of major production companies including Disney, Universal, and Warner Bros. Discovery did file lawsuits against AI firms and their boosters for that very reason. But rather than pummeling AI purveyors into the ground, some of Hollywood’s biggest power players chose instead to get into bed with them. We have only just begun to see what can come from this new era of gen-AI partnerships, but all signs point to things getting much sloppier in the very near future.
Though many of this year’s gen-AI headlines were dominated by larger outfits like Google and OpenAI, we also saw a number of smaller players vying for a seat at the entertainment table. There was Asteria, Natasha Lyonne’s startup focused on developing film projects with “ethically” engineered video generation models, and startups like Showrunner, an Amazon-backed platform designed to let subscribers create animated “shows” (a very generous term) from just a few descriptive sentences plugged into Discord. These relatively new companies were all desperate to legitimize the idea that their flavor of gen AI could be used to supercharge film / TV development while bringing down overall production costs.
Asteria didn’t have anything more than hype to share with the public after announcing its first film, and it was hard to believe that normal people would be interested in paying for Showrunner’s shoddily cobbled-together knockoffs of shows made by actual animators. In the latter case, it felt very much like Showrunner’s real goal was to secure juicy partnerships with established studios like Disney that would lead to their tech being baked into platforms where users could prompt up bespoke content featuring recognizable characters from massive franchises.
That idea seemed fairly ridiculous when Showrunner first hit the scene because its models churn out the modern equivalent of clunky JibJab cartoons. But in due time, Disney made it clear that — crappy as text-to-video generators tend to be for anything beyond quick memes — it was interested in experimenting with that kind of content. In December, Disney entered into a three-year, billion-dollar licensing deal with OpenAI that would let Sora users make AI videos with 200 different characters from Star Wars, Marvel, and more.
Netflix became one of the first big studios to proudly announce that it was going all-in on gen AI. After using the technology to produce special effects for one of its original series, the streamer published a list of general guidelines it wanted its partners to follow if they planned to jump on the slop bandwagon as well. Though Netflix wasn’t mandating that filmmakers use gen AI, it made clear that saving money on VFX work was one of the main reasons it was coming out in support of the trend. And it wasn’t long before Amazon followed suit by releasing multiple Japanese anime series that were terribly localized into other languages because the dubbing process didn’t involve any human translators or voice actors.
Amazon’s gen-AI dubs became a shining example of how poorly this technology can perform. They also highlighted how some studios aren’t putting all that much effort into making sure that their gen AI-derived projects are polished enough to be released to the public. That was also true of Amazon’s machine-generated TV recaps, which frequently got details about different shows very wrong. Both of these fiascos made it seem as if Amazon somehow thought that people wouldn’t notice or care about AI’s inability to consistently generate high-quality outputs. The studio quickly pulled its AI-dubbed series and the recap feature down, but it didn’t say that it wouldn’t try this kind of nonsense again.
All of this and other dumb stunts like AI “actress” Tilly Norwood made it feel like certain segments of the entertainment industry were becoming more comfortable trying to foist gen-AI “entertainment” on people even though it left many people deeply unimpressed and put off. None of these projects demonstrated to the public why anyone except for money-pinching execs (and people who worship them for some reason) would be excited by a future shaped by this technology.
Aside from a few unimpressive images, we still haven’t seen what all might come from some of these collaborations, like Disney cozying up to OpenAI. But next year AI’s presence in Hollywood will be even more pronounced. Disney plans to dedicate an entire section of its streaming service to user-generated content sourced from Sora, and it will encourage Disney employees to use OpenAI’s ChatGPT products. But the deal’s real significance in this current moment is the message it sends to other studios about how they should move as Hollywood enters its slop era.
Regardless of whether Disney thinks this will work out well, the studio has signaled that it doesn’t want to be left behind if AI adoption keeps accelerating. That tells other production houses that they should follow suit, and if that becomes the case, there’s no telling how much more of this stuff we are all going to be forced to endure.
Birdbuddy is introducing two new smart bird feeders: the flagship Birdbuddy 2 and the more compact, cheaper Birdbuddy 2 Mini aimed at first-time users and smaller outdoor spaces. Both models are designed to be faster and easier to use than previous generations, with upgraded cameras that can shoot in portrait or landscape and wake instantly when a bird lands so you’re less likely to miss the good stuff.
The Birdbuddy 2 costs $199 and features a redesigned circular camera housing that delivers 2K HDR video, slow-motion recording, and a wider 135-degree field of view. The upgraded built-in mic should also better pick up birdsong, which could make identifying species easier using both sound and sight.
The feeder itself offers a larger seed capacity and an integrated perch extender, along with support for both 2.4GHz and 5GHz Wi-Fi for more stable connectivity. The new model also adds dual integrated solar panels to help keep it powered throughout the day, while adding a night sleep mode to conserve power.
The Birdbuddy 2 Mini is designed to deliver the same core AI bird identification and camera experience, but in a smaller, more accessible package. At 6.95 inches tall with a smaller seed capacity, it’s geared toward first-time smart birders and smaller outdoor spaces like balconies, and it supports an optional solar panel.
Birdbuddy 2’s first batch of preorders has already sold out, with shipments expected in February 2026 and wider availability set for mid-2026. Meanwhile, the Birdbuddy 2 Mini will be available to preorder for $129 in mid-2026, with the company planning on shipping the smart bird feeder in late 2026.
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Most robot headlines follow a familiar script: a machine masters one narrow trick in a controlled lab, then comes the bold promise that everything is about to change. I usually tune those stories out. We have heard about robots taking over since science fiction began, yet real-life robots still struggle with basic flexibility. This time felt different.
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ELON MUSK TEASES A FUTURE RUN BY ROBOTS
Researchers highlight the milestone that shows how a robot learned 1,000 real-world tasks in just one day. (Science Robotics)
How robots learned 1,000 physical tasks in one day
A new report published in Science Robotics caught our attention because the results feel genuinely meaningful, impressive and a little unsettling in the best way. The research comes from a team of academic scientists working in robotics and artificial intelligence, and it tackles one of the field’s biggest limitations.
The researchers taught a robot to learn 1,000 different physical tasks in a single day using just one demonstration per task. These were not small variations of the same movement. The tasks included placing, folding, inserting, gripping and manipulating everyday objects in the real world. For robotics, that is a big deal.
Why robots have always been slow learners
Until now, teaching robots physical tasks has been painfully inefficient. Even simple actions often require hundreds or thousands of demonstrations. Engineers must collect massive datasets and fine-tune systems behind the scenes. That is why most factory robots repeat one motion endlessly and fail as soon as conditions change. Humans learn differently. If someone shows you how to do something once or twice, you can usually figure it out. That gap between human learning and robot learning has held robotics back for decades. This research aims to close that gap.
THE NEW ROBOT THAT COULD MAKE CHORES A THING OF THE PAST
The research team behind the study focuses on teaching robots to learn physical tasks faster and with less data. (Science Robotics)
How the robot learned 1,000 tasks so fast
The breakthrough comes from a smarter way of teaching robots to learn from demonstrations. Instead of memorizing entire movements, the system breaks tasks into simpler phases. One phase focuses on aligning with the object, and the other handles the interaction itself. This method relies on artificial intelligence, specifically an AI technique called imitation learning that allows robots to learn physical tasks from human demonstrations.
The robot then reuses knowledge from previous tasks and applies it to new ones. This retrieval-based approach allows the system to generalize rather than start from scratch each time. Using this method, called Multi-Task Trajectory Transfer, the researchers trained a real robot arm on 1,000 distinct everyday tasks in under 24 hours of human demonstration time.
Importantly, this was not done in a simulation. It happened in the real world, with real objects, real mistakes and real constraints. That detail matters.
Why this research feels different
Many robotics papers look impressive on paper but fall apart outside perfect lab conditions. This one stands out because it tested the system through thousands of real-world rollouts. The robot also showed it could handle new object instances it had never seen before. That ability to generalize is what robots have been missing. It is the difference between a machine that repeats and one that adapts.
AI VIDEO TECH FAST-TRACKS HUMANOID ROBOT TRAINING
The robot arm practices everyday movements like gripping, folding and placing objects using a single human demonstration. (Science Robotics)
A long-standing robotics problem may finally be cracking
This research addresses one of the biggest bottlenecks in robotics: inefficient learning from demonstrations. By decomposing tasks and reusing knowledge, the system achieved an order of magnitude improvement in data efficiency compared to traditional approaches. That kind of leap rarely happens overnight. It suggests that the robot-filled future we have talked about for years may be nearer than it looked even a few years ago.
What this means for you
Faster learning changes everything. If robots need less data and less programming, they become cheaper and more flexible. That opens the door to robots working outside tightly controlled environments.
In the long run, this could enable home robots to learn new tasks from simple demonstrations instead of specialist code. It also has major implications for healthcare, logistics and manufacturing.
More broadly, it signals a shift in artificial intelligence. We are moving away from flashy tricks and toward systems that learn in more human-like ways. Not smarter than people. Just closer to how we actually operate day to day.
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Kurt’s key takeaways
Robots learning 1,000 tasks in a day does not mean your house will have a humanoid helper tomorrow. Still, it represents real progress on a problem that has limited robotics for decades. When machines start learning more like humans, the conversation changes. The question shifts from what robots can repeat to what they can adapt to next. That shift is worth paying attention to.
If robots can now learn like us, what tasks would you actually trust one to handle in your own life? Let us know by writing to us at Cyberguy.com
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Plaud has updated its compact NotePin AI recorder. The new NotePin S is almost identical to the original, except for one major difference: a button. It’s joined by a new Plaud Desktop app for recording audio in online meetings, which is free to owners of any Plaud Note or NotePin.
The NotePin S has the same FitBit-esque design as the 2024 original and ships with a lanyard, wristband, clip, and magnetic pin, so you can wear it just about any way you please — now all included in the box, whereas before the lanyard and wristband were sold separately.
It’s about the same size as the NotePin, comes in the same colors (black, purple, or silver), offers similar battery life, and still supports Apple Find My. Like the NotePin, it records audio and generates transcriptions and summaries, whether those are meeting notes, action points, or reminders.
But now it has a button. Whereas the first NotePin used haptic controls, relying on a long squeeze to start recording, with a short buzz to let you know it worked, the S switches to something simpler. A long press of the button starts recording, a short tap adds highlight markers. Plaud’s explanation for the change is simple: buttons are less ambiguous, so you’ll always know you’ve successfully pressed it and started recording, whereas original NotePin users complained they sometimes failed to record because they hadn’t squeezed just right.
AI recorders like this live or die by ease of use, so removing a little friction gives Plaud better odds of survival.
Alongside the NotePin S, Plaud is launching a new Mac and PC application for recording the audio from online meetings. Plaud Desktop runs in the background and activates whenever it detects calls from apps including Zoom, Meet, and Teams, recording both system audio and from your microphone. You can set it to either record meetings automatically or require manual activation, and unlike some alternatives it doesn’t create a bot that joins the call with you.
Recordings and notes are synced with those from Plaud’s line of hardware recorders, with the same models used for transcription and generation, creating a “seamless” library of audio from your meetings, both online and off.
Plaud Desktop is available now and is free to anyone who already owns a Plaud Note or NotePin device. The new NotePin S is also available today, for $179 — $20 more than the original, which Plaud says will now be phased out.
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