The Secret Race To Produce the First AI-Generated Blockbuster Movie

Movie-making is an artifice, so will creators embrace AI?

While many actors and filmmakers go out of their way to bash AI image-generating technology as hard as they can, there’s a secret undercurrent in which Hollywood is all-in on Artificial Intelligence, and the race is on to generate the first AI blockbuster.

Some sectors of the film industry are slow to accept change, with some still refusing to change 35mm film for digital.

The fact is: when creators shun utilizing new tech developments, they are just marrying themselves to an earlier-age’s technological solutions – after all, there’s nothing natural about film-making.

The New York Post reported:

“AI has officially been met with extreme skepticism by more entrenched filmmakers. However — as is always the way in two-faced, back-biting, cutthroat Hollywood — that also means behind the scenes, every studio, filmmaker, and screenwriter worth their salt are desperately trying to harness its power to push the boundaries and create something new to wow audiences with.

‘The space race was about being first, but it was also about inspiring the world that we should keep competing and keep investing [in the technology]. Bigger than being first, which is fleeting, is inspiring the world’, Bryn Mooser, who has two Academy Award nominations for documentary shorts, told The Post.”

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AI war machines hit the ground running: Marine Corps quietly orders autonomous ground vehicle fleet that can drive itself into combat

The United States Marine Corps has quietly placed its first production order for a fleet of fully autonomous ground vehicles that can navigate battlefields, plot their own routes, and execute resupply missions without a human behind the wheel. This is no longer science fiction. Under a $19.7 million production award issued through the Pentagon’s Accelerate the Procurement and Fielding of Innovative Technologies program, Seattle-based Overland AI will deliver more than a dozen autonomous ground vehicles to the Marine Corps by early 2027, marking the first time a ground autonomy company has served as the prime contractor for a production contract of its kind. While military officials frame this as a logistical upgrade, the deeper implications point toward a future where machines not only drive themselves but eventually decide when and how to kill, and in the most efficient manner possible.

Key points:

  • The Marine Corps has awarded Overland AI a $19.7 million contract for autonomous ground vehicles.
  • Vehicles will operate without continuous human control using onboard navigation software.
  • First operational role focuses on resupply missions for the Marine Air Defense Integrated System.
  • Overland AI CEO Byron Boots confirmed extremely high demand from U.S. operational units.
  • Autonomous platforms could expand into intelligence, surveillance, and breaching missions.
  • Contract delivered through Pentagon APFIT program designed to accelerate battlefield technology.
  • Vehicles use open architecture allowing integration with Joint Light Tactical Vehicle platforms.

When machines decide where to go

What separates Overland AI’s platform from previous unmanned ground vehicles is the degree of independence these machines possess. Traditional robotic vehicles require a remote operator to steer, accelerate, and brake, essentially a video game controller attached to a military vehicle. Overland AI’s system operates differently. Operators assign a destination point, and the onboard software handles everything else. The vehicle plans its own route, interprets terrain conditions, controls acceleration and braking, and navigates obstacles without continuous human input. Personnel can still assume remote control when necessary, but the default mode is machine autonomy.

This represents a fundamental shift in military robotics. The Marine Corps is no longer testing remote-controlled equipment. It is buying vehicles that make driving decisions on their own. Overland AI Chief Executive Officer Byron Boots stated that demand for autonomous ground systems has increased sharply as militaries evaluate lessons from recent conflicts. “Ground autonomy matters now more than ever,” Boots said. “We’re registering extremely high demand from U.S. operational units who want to incorporate this technology into their concepts of operation.”

The company expects to deliver the vehicles in roughly nine months, though officials did not disclose the exact number of platforms or technical specifications including payload capacity and vehicle type. This opacity is deliberate. The military does not want adversaries knowing how many autonomous systems are entering the battlefield or what they are capable of carrying.

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While The Political Circus Distracts Us, Flock Builds The Digital Police State

“You had to live – did live, from habit that became instinct – in the assumption that every sound you made was overheard, and, except in darkness, every movement scrutinized.”

– George Orwell, 1984

While Americans remain transfixed by the political circus – cheering for their preferred party, jeering at the opposition, obsessing over every manufactured outrage and waiting for the next spectacle – the Surveillance State continues its steady march forward.

The government is watching.

It watches where you go, whom you meet, where you worship, what medical offices you visit, what political rallies you attend, what protests you join, what books you read, what websites you visit and what causes you support.

It watches through your phone, your car, your doorbell, your appliances, your purchases, your social media accounts and the cameras positioned along the roads you travel every day.

This is how freedom dies in the digital police state: not always through dramatic declarations of martial law or soldiers stationed on every street corner, but through the gradual construction of a technological dragnet—an electronic concentration camp—so pervasive that privacy becomes impossible and anonymity becomes suspicious.

Enter Flock Safety, a private surveillance technology company whose automated license plate readers have spread throughout thousands of American communities.

These cameras, which do much more than photograph license plates, represent the next evolution of the government’s public-private surveillance partnership.

They document the time and location of every passing vehicle and record identifying characteristics such as its make, model, color, damage, roof racks, bumper stickers and other distinctive features. That information can then be placed in a searchable database and used to retrace a vehicle’s movements over time.

Yet the real power—and the real danger—of Flock does not come from the cameras alone.

It comes from artificial intelligence.

A camera can photograph a car. Flock’s AI-powered platform can identify and categorize a vehicle, compare an observation with stored records, generate alerts, identify connections and help police reconstruct where that vehicle has been.

AI is what transforms a photograph into the building blocks for a suspect society.

With AI, every driver becomes a data point. Every data point becomes a pattern. And every pattern becomes a suspicion.

This is how ordinary movements become potentially suspect and subject to government scrutiny. It allows law enforcement agencies to search not only for a complete license plate number but also for partial plates and physical descriptions such as vehicle color, make, model, damage, roof racks, bumper stickers and other identifying characteristics.

A police officer might ask the system to locate every red pickup truck with a ladder rack seen near a protest, every vehicle that repeatedly visited a particular address, or every car observed traveling between two locations.

The artificial intelligence does the sorting. The database supplies the history.

The government receives a list of potential suspects.

This is no longer surveillance conducted by individual officers following particular leads. It is surveillance conducted at machine speed, across entire populations, with algorithms deciding whose movements merit further scrutiny.

Consider the scale of what is taking place.

License plate cameras now log approximately 20 billion vehicle scans every month.

Twenty billion.

That is not targeted policing. That is mass collection.

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Quote of the day by Sun Microsystems CEO Scott McNealy: ‘You have zero privacy anyway. Get over it’ — an early declaration foreshadowing the modern era

Sun Microsystems was a huge force in the technology landscape, with its co-founder and CEO Scott McNealy an outspoken and brash maverick in the early Silicon Valley ecosystem. The company had just launched a new system, and McNealy was quick to push back on any critique centering around the implications for user data.

During an informal Q&A session with reporters, McNealy slapped down concerns that the newly launched Jini platform could pose a risk to user privacy.

The system, as it was engineered, was a revelation – but ultimately failed to catch on due to some pretty significant hardware hurdles. Designed to allow devices to communicate with and share resources, the Jini network architecture allowed unadulterated communication without configuration, driver installations, or human intervention.

It was an early and ambitious effort to establish a vision for smart homes and offices. The trouble was that it required devices to continuously upload data and lease space on networks, with the system creating a massive digital footprint.

Erosion of privacy

McNealy’s comments, unsurprisingly, drew immediate and sharp criticism from privacy advocates and campaigners. Lori Fena, then chairman of the board of the Electronic Frontier Foundation, said the comments were “completely irresponsible”, for example.

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Humanoid Robots Perform Successful Gallbladder Surgery in Major Medical Breakthrough

Humanoid robots have successfully performed gallbladder removal surgeries in live pigs for the first time, marking a significant milestone in the development of robotic surgery and paving the way for future human trials.

The procedures were carried out by researchers at the University of California, San Diego, with the findings published Wednesday in the journal Nature.

The first operation involved a humanoid robot working alongside a surgeon. In the second, two humanoid robots completed the procedure together without direct human assistance.

Researchers say the successful surgeries represent an important proof of concept as the technology moves toward clinical testing in humans.

“As a proof of concept, it absolutely worked,” Dr. Ryan Broderick, interim director of the Center for the Future of Surgery at UC San Diego, told ABC News.

Unlike conventional robotic surgical platforms, the humanoid robots feature a head and two arms.

This allows them to work in operating rooms without the bulky equipment typically required for robot-assisted procedures.

“The space constraints didn’t exist like in traditional robotic surgery,” Broderick said.

“It was a human-type bedside assistant, so it just fit into the space that we’re traditionally used to being in for laparoscopic surgery.”

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Without Subsidies, Is AI Unaffordable?

Let’s pull all this into an undeniable conclusion: AI is based on massively subsidizing users’ costs.

What’s already abundantly clear but verboten to say as it would pop the bubble of AI valuations and triumphalism is that AI is unaffordable once the direct and indirect subsidies are withdrawn. Nothing that consumes this much electricity and requires such an immense scale of costly processing and memory capacity can be low-cost, never mind free.

The major AI platforms and vendors are subsidizing corporate and individual users in the hopes that they can achieve AI sector dominance –and the pricing power that comes with it–via the network effect, the dominance generated by having the majority of users bound by habit or dependence to your platform or tools.

This battle for network effect dominance is playing out in full view:

AI Giants Are Handing Out Tons of Free Computing Power to Grab Startup Share: (wsj.com) Pitched battle for business users comes as AI companies seek lasting streams of revenue.

Hans Ibarra, a founder building an AI-voice startup, has found himself on the receiving end of a big opportunity: Top artificial-intelligence companies such as OpenAI, Anthropic and others desperate to win his business are ramping up discounts.

Across Silicon Valley, startup founders like Ibarra are enjoying a wave of computing credits and fielding competing offers from AI-model makers racing to land new enterprise customers. Cursor, the AI-coding company bought by Elon Musk’s SpaceX, offered a 75% discount through July 5.

“If I’m choosing between a really cheap Chinese model that I actually have to pay for, and a very expensive Anthropic model that I don’t have to pay for, I’m going to pick the Anthropic model,” Acker said. “I’m always going to pick the one for which I have free credits.”

Meanwhile, back in the real world of costs, AI Costs More Than The People It Replaced (forbes.com)(via Tom D.)

It turns out that experienced human workers doing the work right in the first place is cheaper than having AI run a probability distribution process that needs vetting and corrections. And remember, AI isn’t actually “intelligent,” it’s just a probability distribution using natural language.

As management guru Peter Drucker observed, enterprises don’t have profits, they have costs. Purveyors of AI platforms and tools have costs, and so do their customers. Those costs are currently being funded by investors, who are in effect subsidizing the AI companies’ “free” giveaways of horrendously costly “tokens” in a manic, desperate attempt to grab the brass ring of network effect dominance before their cash runs out.

This raises a question: Is this any way to run a railroad? In other words, is this actually a viable business model, burning billions of dollars in cash to lock in network effect dominance in a field that is rapidly obsoleting every iteration of an innately limited mode of computation? Is claiming that a probability distribution is “intelligent” in the same way humans are intelligent a viable business model when there is ample evidence this simply isn’t true?

AI and human intelligence are drastically different–here’s how (scientificamerican.com)

What happens when enterprises have to pay the unsubsidized costs of AI is they immediately curtail their AI spending because the customer-facing / financial benefits of AI are at best elusive and often negative. Peter Drucker was onto something that is currently being lost in the PR-propaganda push of those trying to cash in on the AI euphoria: enterprises don’t have profits, they have costs, and the real-world costs of AI are extraordinarily high while the payoffs are ambiguous.

There are many other hidden subsidies within the AI machinery. There are corporate tax write-off subsidies, energy subsidies, tax credit subsidies for building data centers, and so on. If these were stripped out, what would the real unsubsidized costs of AI be? No one knows, but they would be higher than what’s presented as the cost now.

Then there’s the if it’s legal, it’s moral, and what’s legal is for sale subsidy: AI is built on the systemic theft of copyrighted content. Last month alone, AI scrapers gorged on 246,000 pages from my Of Two Minds server, and hundreds of thousands of pages of my copyrighted works on my mirror site and other sites posting my work.

This is legal, but is it moral? Nobody asks such questions because the important thing is to avoid saddling AI users with the real costs. So if all those content creators get nothing–in effect, subsidizing both AI companies and the users of their AI platforms and tools–well, so what, because if it’s legal, it’s moral, and what’s legal is for sale.

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The Economics Of The Surveillance State

How did KGB agents commit suicide?  Two shots to the back of the head.  (all photo content as-found)

Remember Lavrentiy Beria’s cheerful advice:  “Show me the man, and I will find the crime”?  Back in the Soviet Union they had so many laws on the books that everybody broke at least one before lunch, I mean, when lunch was available.  And if they didn’t, they could make up something.  Beria just needed enough spies and informants to spot the right violation.

Beria would have loved modern America.  We’ve upgraded his whole operation with better cameras, faster computers, and added actual profit margins.

Let’s start with Flock™ cameras.

Flock Safety© cameras now line roads from coast to coast.  More than 100,000 of the little snitches sit on poles in ditches scanning license plates 24/7 and however many metric hours in a metric day and metric days in a metric week.  The cameras rolled out one quiet law enforcement contract at a time until the whole country is now blanketed.

Not everyone who comes into your life is your friend.  Some are just surveillance cameras. (btw, she was innocent, but the police didn’t apologize)

Maps of the cameras exist online, but those rely on humans, and it shows only three of the eight within five miles of my house in Modern Mayberry.  I could plot an avoidance route if I had nothing better to do than play spy versus spy on my commute, or build a detector like Benn Jordan did.

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AI Price War Breaks Out: Meta Unveils Paid AI Model For First Time, Will Be “Among Most Affordable Options”

Shortly after a leaked Meta memo revealed the company was planning on putting an AI chip into production in September as it looks to double computing capacity to 14Gigawatts, the company also unveiled a version of its most advanced artificial intelligence model, Muse Spark 1.1, that includes a new paid tier for developersmarking the first time Meta has charged businesses for access to its models and providing a new revenue stream. It’ll be among the most affordable options on the market, Zuckerberg said in a Bloomberg interview ahead of the release.

“Since this is not an open source model, this is I think the first time that we’re doing a real serious API,” Zuckerberg said, referring to the application programming interface used to access Meta’s AI. “And the pricing is going to be very aggressive and attractive” he added indicating that Meta hopes to capture market share by undercutting its competitors, offering the new model at 25% of the cost of top models from OpenAI and Anthropic.

The new model’s biggest improvement is in its agentic capabilities, the Meta CEO told Bloomberg, and according to benchmarks the model does indeed appear to be in line with the competition.

He hopes to piggyback on the latest craze in AI development this year, which a month ago saw Goldman forecast that agentic AI use will lead to a massive 120 quadrillion monthly tokens being used by 2030.

Agents are the big theme of AI this year, with the label applied to systems that can complete multistep tasks on behalf of a user. Zuckerberg described Muse Spark 1.1 as having “state-of-the-art or very close to it” agentic reasoning and tool use. The model is also greatly improved when it comes to coding and Meta employees are using it internally to build products and features for various apps, he added. 

Meta will also introduce a new Meta Model API system, which will be used to collect fees from developers. Its API pricing is roughly 25% of the cost advertised by other top models from OpenAI and Anthropic, according to Bloomberg. Developers will be able to use Meta’s model for free, but only up to a point; they’ll be required to pay for access after reaching a certain token threshold, Zuckerberg said. 

Which means that legacy frontier models will now have to worry about domestic cheap alternativesespecially after xAI also released an agentic and coding model yesterday which will have to grab market share, in addition to much cheaper Chinese models.

“The pricing from some of the other labs is very extreme and has very high margins,” Zuckerberg said, underscoring that his strategy is to get Meta’s technology in front of as many people as possible. “We think that there’s a real ability to be able to offer frontier or very high-level intelligence at a much more affordable cost.”

Zuckerberg, 42, is spending aggressively to keep pace with rivals like OpenAI and Alphabet in a race to achieve what he calls superintelligence, or AI that can perform tasks better than humans. Meta has committed hundreds of billions of dollars to building the infrastructure necessary to develop superintelligence, including data centers and expensive AI chips. The company announced a new $10 billion data center investment in Canada as well as a new image-generation model just this week.

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Leaked Meta Memo Shows AI Capacity Doubling To 14 Gigawatts

Meta shares fell 4.3% at Thursday’s open after Reuters reported the contents of an internal memo laying out the next phase of the company’s AI infrastructure program.

The stock has clawed back part of the loss through the morning but stayed solidly red while the tape digested the same question it has been chewing on for nine days: is Meta the hyperscaler that just started exercising capex discipline, or the one that just committed to doubling?

Three things to note from today’s news. The first is silicon. Iris, Meta’s in-house AI accelerator and one of four planned MTIA generations unveiled in Marchenters production at TSMC in September after clearing bug validation in six weeks with no major issues – an unusually clean result for a program that has stumbled for more than half a decade. Broadcom is the design partner under an agreement extended through 2029, and Meta plans to ship a new chip roughly every six months through 2027, against an industry norm of annual-or-slower cadences. The chips are meant to augment, not replace, externally sourced GPUs – Meta separately holds a multiyear agreement with AMD covering up to six gigawatts of Instinct accelerators – but the internal memo is very blunt about why the program matters – as adopting the latest external GPUs at Meta’s scale “has been a heavy lift, and it has cost us time.”

The second is scale. Meta plans to deploy seven gigawatts of computing infrastructure this year and to double overall capacity to fourteen gigawatts in 2027, with 2026 spending running as high as $145 billion – the very top of the range guided in April, and a meaningful slice of the more than $700 billion Big Tech is projected to pour into AI this year.

The third is supply. The memo reveals long-term contracts for memory from Samsung, flash storage from Sandisk and fiber-optic equipment from Sumitomo Electric – multi-year lock-ins struck in the middle of a memory shortage severe enough to be raising consumer hardware prices.

On its face the chip news is bullish: faster, cheaper, more independent compute is exactly what a company spending $145 billion a year should want. But the market has spent the past week and a half developing a very specific allergy, and the memo triggered it.

When Bloomberg reported at the start of the month that Meta was standing up a cloud business – internally, Meta Compute – to sell surplus capacity and token-metered API access to outsiders, the stock ripped nearly 9% higher in a session while CoreWeave and Nebius fell double digits. We suggested this might be a potential first crack in the AI capex boom: hoarding compute stops making sense the moment you admit you have extra, and if management appears willing to monetize idle infrastructure, the market reads capital discipline and pays for it. Days later, leaked town-hall remarks in which Zuckerberg conceded that agent development “hasn’t accelerated in the way we expected” knocked the stock back down – the July 2 drop that Thursday’s open just eclipsed.

Against that backdrop, a memo describing a doubling of capacity, a six-month silicon cadence and years of locked-in component supply looks rather – undisciplined when it comes to capex. Companies do not sign multi-year memory contracts in the middle of a shortage in order to stand still. As we noted earlier this month – the pivot to rewarding CapEx cutters – has, for now, been a driving force: up on plans to sell capacity, down on plans to double it, with the same infrastructure underneath both headlines.

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How Flock Cameras Wrongly Tracked Me for Days Over ‘Stolen’ Plates and Sent Police After Me

Are you armed?!” the police officer screamed. “Get out of the car!”

On an otherwise normal Sunday afternoon in late June, I’d decided to take the $155,000 Range Rover I was testing that week out to run some errands with my wife. Little did I know that choice would complete a technological chain linking surveillance cameras, AI, and law enforcement that led to me and my wife being surrounded by police, hands on their guns, in a Kohl’s parking lot in suburban Minnesota.

After dropping off our Amazon returns, we’d just gotten back in the Range Rover and reversed maybe two feet out of the spot when four cop cars came flying out of nowhere and boxed us in. The officers jumped out and started shouting. It’s a situation that can quickly and frequently turn bad, so as unprepared as I was, I followed their orders, got out with my hands up, and tried to figure out what the hell was happening.

Eventually, after a tense hour, I did. The Plymouth Police Department had been tracking me for days using Flock license plate cameras, waiting for the right moment to strike, because they thought I’d stolen the Range Rover. And the reason I was ID’d as a dangerous car thief was a simple data error made 2,000 miles away in California, creating an edge case within an edge case that Flock’s AI camera network was unable to handle.

We now live in a surveillance state where cameras mounted on stoplights are tracking our cars, our devices, our pets, and even us. This is just the beginning; next, these cameras could be put in motion using our kids’ school buses. Whether you’ve actually stolen a car or are just rolling down the road having done nothing wrong, like me, once these systems have you in their crosshairs, there’s pretty much only one way it can go. Welcome to the future. It’s scary out there.

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