Paul Patrick – Page 2

The first new Marathon game in decades will launch on March 5

Bungie, delay, destiny, Development, extraction shooter, gaming, Halo, marathon, ps5, release date, steam, xbox series / Paul Patrick / January 19, 2026

It’s been nearly three years now since Destiny maker (and Sony subsidiary) Bungie formally announced a revival of the storied Marathon FPS franchise. And it has been about seven months since the game’s original announced release date of September 23, 2025 was pushed back indefinitely after a reportedly poor response to the game’s first Alpha test.

But today, in a post on the PlayStation Blog, Bungie revealed that the new Marathon would finally be hitting PS5, Windows, and Xbox Series X|S on March 5, narrowing down the month-long March release window announced back in December.

Today’s pre-rder trailer revealing the Marathon release date.

Unlike Destiny 2, which transitioned to a free-to-play model in 2019, the new Marathon sells for $40 in a Standard Edition or a $60 Deluxe Edition that includes some digital rewards and cosmetics. That mirrors the pricing of the somewhat similar Arc Raiders, which recently hit 12 million sales in less than 12 weeks.

A new kind of Marathon

Unlike the original Marathon trilogy on the ’90s Macintosh—which closely followed on the single-player campaign corridors and deathmatch multiplayer of the original Doom—the new Marathon is described as a “PvPvE survival extraction shooter.” That means gameplay based around exploring distinct zones and scavenging for cosmetics and gear upgrades in exploratory missions alone or with up to two friends, then seeing those missions “break into fast-paced PvP combat” at a moment’s notice, according to the game’s official description.

The first new Marathon game in decades will launch on March 5 Read More »

$elon-musk-accused-of-making-up-math-to-squeeze-$134b-from-openai,-microsoft$

Elon Musk accused of making up math to squeeze $134B from OpenAI, Microsoft

AI, elon musk, microsoft, openai, Policy, sam altman, xAI / Paul Patrick / January 19, 2026

Musk’s math reduced ChatGPT inventors’ contributions to “zero,” OpenAI argued.

Elon Musk is going for some substantial damages in his lawsuit accusing OpenAI of abandoning its nonprofit mission and “making a fool out of him” as an early investor.

On Friday, Musk filed a notice on remedies sought in the lawsuit, confirming that he’s seeking damages between $79 billion and $134 billion from OpenAI and its largest backer, co-defendant Microsoft.

Musk hired an expert he has never used before, C. Paul Wazzan, who reached this estimate by concluding that Musk’s early contributions to OpenAI generated 50 to 75 percent of the nonprofit’s current value. He got there by analyzing four factors: Musk’s total financial contributions before he left OpenAI in 2018, Musk’s proposed equity stake in OpenAI in 2017, Musk’s current equity stake in xAI, and Musk’s nonmonetary contributions to OpenAI (like investing time or lending his reputation).

The eye-popping damage claim shocked OpenAI and Microsoft, which could also face punitive damages in a loss.

The tech giants immediately filed a motion to exclude Wazzan’s opinions, alleging that step was necessary to avoid prejudicing a jury. Their filing claimed that Wazzan’s math seemed “made up,” based on calculations the economics expert testified he’d never used before and allegedly “conjured” just to satisfy Musk.

For example, Wazzan allegedly ignored that Musk left OpenAI after leadership did not agree on how to value Musk’s contributions to the nonprofit. Problematically, Wazzan’s math depends on an imaginary timeline where OpenAI agreed to Musk’s 2017 bid to control 51.2 percent of a new for-profit entity that was then being considered. But that never happened, so it’s unclear why Musk would be owed damages based on a deal that was never struck, OpenAI argues.

It’s also unclear why Musk’s stake in xAI is relevant, since OpenAI is a completely different company not bound to match xAI’s offerings. Wazzan allegedly wasn’t even given access to xAI’s actual numbers to help him with his estimate, only referring to public reporting estimating that Musk owns 53 percent of xAI’s equity. OpenAI accused Wazzan of including the xAI numbers to inflate the total damages to please Musk.

“By all appearances, what Wazzan has done is cherry-pick convenient factors that correspond roughly to the size of the ‘economic interest’ Musk wants to claim, and declare that those factors support Musk’s claim,” OpenAI’s filing said.

Further frustrating OpenAI and Microsoft, Wazzan opined that Musk and xAI should receive the exact same total damages whether they succeed on just one or all of the four claims raised in the lawsuit.

OpenAI and Microsoft are hoping the court will agree that Wazzan’s math is an “unreliable… black box” and exclude his opinions as improperly reliant on calculations that cannot be independently tested.

Microsoft could not be reached for comment, but OpenAI has alleged that Musk’s suit is a harassment campaign aimed at stalling a competitor so that his rival AI firm, xAI, can catch up.

“Musk’s lawsuit continues to be baseless and a part of his ongoing pattern of harassment, and we look forward to demonstrating this at trial,” an OpenAI spokesperson said in a statement provided to Ars. “This latest unserious demand is aimed solely at furthering this harassment campaign. We remain focused on empowering the OpenAI Foundation, which is already one of the best resourced nonprofits ever.”

Only Musk’s contributions counted

Wazzan is “a financial economist with decades of professional and academic experience who has managed his own successful venture capital firm that provided seed-level funding to technology startups,” Musk’s filing said.

OpenAI explained how Musk got connected with Wazzan, who testified that he had never been hired by any of Musk’s companies before. Instead, three months before he submitted his opinions, Wazzan said that Musk’s legal team had reached out to his consulting firm, BRG, and the call was routed to him.

Wazzan’s task was to figure out how much Musk should be owed after investing $38 million in OpenAI—roughly 60 percent of its seed funding. Musk also made nonmonetary contributions Wazzan had to weigh, like “recruiting key employees, introducing business contacts, teaching his cofounders everything he knew about running a successful startup, and lending his prestige and reputation to the venture,” Musk’s filing said.

The “fact pattern” was “pretty unique,” Wazzan testified, while admitting that his calculations weren’t something you’d find “in a textbook.”

Additionally, Wazzan had to factor in Microsoft’s alleged wrongful gains, by deducing how much of Microsoft’s profits went back into funding the nonprofit. Microsoft alleged Wazzan got this estimate wrong after assuming that “some portion of Microsoft’s stake in the OpenAI for-profit entity should flow back to the OpenAI nonprofit” and arbitrarily decided that the portion must be “equal” to “the nonprofit’s stake in the for-profit entity.” With this odd math, Wazzan double-counted value of the nonprofit and inflated Musk’s damages estimate, Microsoft alleged.

“Wazzan offers no rationale—contractual, governance, economic, or otherwise—for reallocating any portion of Microsoft’s negotiated interest to the nonprofit,” OpenAI’s and Microsoft’s filing said.

Perhaps most glaringly, Wazzan reached his opinions without ever weighing the contributions of anyone but Musk, OpenAI alleged. That means that Wazzan’s analysis did not just discount efforts of co-founders and investors like Microsoft, which “invested billions of dollars into OpenAI’s for-profit affiliate in the years after Musk quit.” It also dismissed scientists and programmers who invented ChatGPT as having “contributed zero percent of the nonprofit’s current value,” OpenAI alleged.

“I don’t need to know all the other people,” Wazzan testified.

Musk’s legal team contradicted expert

Wazzan supposedly also did not bother to quantify Musk’s nonmonetary contributions, which could be in the thousands, millions, or billions based on his vague math, OpenAI argued.

Even Musk’s legal team seemed to contradict Wazzan, OpenAI’s filing noted. In Musk’s filing on remedies, it’s acknowledged that the jury may have to adjust the total damages. Because Wazzan does not break down damages by claims and merely assigns the same damages to each individual claim, OpenAI argued it will be impossible for a jury to adjust any of Wazzan’s black box calculations.

“Wazzan’s methodology is made up; his results unverifiable; his approach admittedly unprecedented; and his proposed outcome—the transfer of billions of dollars from a nonprofit corporation to a donor-turned competitor—implausible on its face,” OpenAI argued.

At a trial starting in April, Musk will strive to convince a court that such extraordinary damages are owed. OpenAI hopes he’ll fail, in part since “it is legally impossible for private individuals to hold economic interests in nonprofits” and “Wazzan conceded at deposition that he had no reason to believe Musk ‘expected a financial return when he donated… to OpenAI nonprofit.’”

“Allowing a jury to hear a disgorgement number—particularly one that is untethered to specific alleged wrongful conduct and results in Musk being paid amounts thousands of times greater than his actual donations—risks misleading the jury as to what relief is recoverable and renders the challenged opinions inadmissible,” OpenAI’s filing said.

Wazzan declined to comment. xAI did not immediately respond to Ars’ request to comment.

Ashley is a senior policy reporter for Ars Technica, dedicated to tracking social impacts of emerging policies and new technologies. She is a Chicago-based journalist with 20 years of experience.

Elon Musk accused of making up math to squeeze $134B from OpenAI, Microsoft Read More »

Asus confirms its smartphone business is on indefinite hiatus

android, ASUS, smartphones, Tech / Paul Patrick / January 19, 2026

An unconfirmed report early this month suggested Asus was pulling back on its smartphone plans, but the company declined to comment at the time. Asus chairman Jonney Shih has now confirmed the wind-down of its smartphone business during an event in Taiwan. Instead, Asus will focus on AI products like robots and smart glasses.

Shih addressed the company’s future plans during a 2026 kick-off event in Taiwan, as reported by Inside. “Asus will no longer add new mobile phone models in the future,” said Shih (machine translated).

So don’t expect a new Zenfone or ROG Phone from Asus in 2026. That said, very few phone buyers were keeping tabs on the latest Asus phones anyway, which is probably why Asus is throwing in the towel. Shih isn’t saying Asus won’t ever release a new phone, but the company will take an “indefinite wait-and-see” approach. Again, this is a translation and could be interpreted in multiple ways.

The Zenfone line might not be missed—its claim to fame was being slightly smaller and cheaper than competing devices, but Asus’ support and update policy were lightyears behind the market leaders. The ROG Phone line has been prominent in the gaming phone niche, offering the latest chipsets with active cooling, multiple USB-C ports, game controller accessories, blinking lights, and even a headphone jack. However, ROG Phones are even more expensive than Samsung’s flagship devices, with the most recent ROG Phone 9 Pro starting at $1,200. Apparently, the market of those who aren’t happy gaming on the latest iPhone or Samsung Galaxy is miniscule.

Existing Asus devices should continue to get updates, but Asus never took the lead there. The lavishly expensive ROG Phone 9 Pro is only guaranteed two OS updates and five years of security patches. The most recent Zenfones are also only eligible for two Android version updates, but they get just four years of security support.

A tough business

Shih’s comments imply that Asus won’t get back into the phone game unless something changes, and that’s not likely. Asus is not the first OEM to drop phone plans, and this is a continuation of a trend that has been underway for years as people upgrade phones less often.

Asus confirms its smartphone business is on indefinite hiatus Read More »

Managers on alert for “launch fever” as pressure builds for NASA’s Moon mission

artemis, artemis II, human spaceflight, Kennedy Space Center, moon, NASA, orion, Science, Space, space launch system / Paul Patrick / January 17, 2026

“Putting crew on the rocket and taking the crew around the Moon, this is going be our first step toward a sustained lunar presence,” Honeycutt said. “It’s 10 days [and] four astronauts going farther from Earth than any other human has ever traveled. We’ll be validating the Orion spacecraft’s life support, navigation and crew systems in the really harsh environments of deep space, and that’s going to pave the way for future landings.”

NASA’s 322-foot-tall (98-meter) SLS rocket inside the Vehicle Assembly Building on the eve of rollout to Launch Complex 39B. Credit: NASA/Joel Kowsky

There is still much work ahead before NASA can clear Artemis II for launch. At the launch pad, technicians will complete final checkouts and closeouts before NASA’s launch team gathers in early February for a critical practice countdown. During this countdown, called a Wet Dress Rehearsal (WDR), Blackwell-Thompson and her team will oversee the loading of the SLS rocket’s core stage and upper stage with super-cold liquid hydrogen and liquid oxygen propellants.

The cryogenic fluids, particularly liquid hydrogen, gave fits to the Artemis launch team as NASA prepared to launch the Artemis I mission—without astronauts—on the SLS rocket’s first test flight in 2022. Engineers resolved the issues and successfully launched the Artemis I mission in November 2022, and officials will apply the lessons for the Artemis II countdown.

“Artemis I was a test flight, and we learned a lot during that campaign getting to launch,” Blackwell-Thompson said. “And the things that we’ve learned relative to how to go load this vehicle, how to load LOX (liquid oxygen), how to load hydrogen, have all been rolled in to the way in which we intend to do for the Artemis II vehicle.”

Finding the right time to fly

Assuming the countdown rehearsal goes according to plan, NASA could be in a position to launch the Artemis II mission as soon as February 6. But the schedule for February 6 is tight, with no margin for error. Officials typically have about five days per month when they can launch Artemis II, when the Moon is in the right position relative to Earth, and the Orion spacecraft can follow the proper trajectory toward reentry and splashdown to limit stress on the capsule’s heat shield.

In February, the available launch dates are February 6, 7, 8, 10, and 11, with launch windows in the overnight hours in Florida. If the mission isn’t off the ground by February 11, NASA will have to stand down until a new series of launch opportunities beginning March 6. The space agency has posted a document showing all available launch dates and times through the end of April.

John Honeycutt, chair NASA’s Mission Management Team for the Artemis II mission, speaks during a news conference at Kennedy Space Center in Florida on January 16, 2026. Credit: Jim Watson/AFP via Getty Images

NASA’s leaders are eager for Artemis II to fly. NASA is not only racing China, a reality the agency’s former administrator acknowledged during the Biden administration. Now, the Trump administration is pushing NASA to accomplish a human landing on the Moon by the end of his presidential term on January 20, 2029.

One of Honeycutt’s jobs as chair of the Mission Management Team (MMT) is ensuring all the Is are dotted and Ts are crossed amid the frenzy of final launch preparations. While the hardware for Artemis II is on the move in Florida, the astronauts and flight controllers are wrapping up their final training and simulations at Johnson Space Center in Houston.

“I think I’ve got a good eye for launch fever,” he said Friday.

“As chair of the MMT, I’ve got one job, and it’s the safe return of Reid, Victor, Christina, and Jeremy. I consider that a duty and a trust, and it’s one I intend to see through.”

Managers on alert for “launch fever” as pressure builds for NASA’s Moon mission Read More »

Rocket Report: Ariane 64 to debut soon; India has a Falcon 9 clone too?

launch, rocket report, Space / Paul Patrick / January 17, 2026

All the news that’s fit to lift

“We are fundamentally shifting our approach to securing our munitions supply chain.”

SpaceX launched the Pandora satellite for NASA on Sunday. Credit: SpaceX

Welcome to Edition 8.25 of the Rocket Report! All eyes are on Florida this weekend as NASA rolls out the Space Launch System rocket and Orion spacecraft to its launch site in Florida for the Artemis II mission. NASA has not announced a launch date yet, and this will depend in part on how well a “wet dress rehearsal” goes with fueling the rocket. However, it is likely the rocket has a no-earlier-than launch date of February 8. Our own Stephen Clark will be in Florida for the rollout on Saturday, so be sure and check back here for coverage.

As always, we welcome reader submissions, and if you don’t want to miss an issue, please subscribe using the box below (the form will not appear on AMP-enabled versions of the site). Each report will include information on small-, medium-, and heavy-lift rockets as well as a quick look ahead at the next three launches on the calendar.

MaiaSpace scores a major launch deal. The ArianeGroup subsidiary, created in 2022, has inked a major new launch contract with satellite operator Eutelsat, Le Monde reports. A significant portion of the 440 new satellites ordered by Eutelsat from Airbus to renew or expand its OneWeb constellation will be launched into orbit by the new Maia rocket. MaiaSpace previously signed two contracts: one with Exotrail for the launch of an orbital transfer, and the other for two satellites for the Toutatis mission, a defense system developed by U-Space.

A big win for the French firm … The first test launch of Maia is scheduled for the end of 2026, a year later than initially planned, at the Guiana Space Centre in French Guiana. The first flights carrying OneWeb satellites are therefore likely to launch no earlier than 2027. Powered by liquid oxygen-methane propellant, Maia aims to be able to deliver up to 500 kg to low-Earth orbit when the first stage is recovered, and 1,500 kg when fully expendable.

Firefly announces Alpha upgrade plan. Firefly Aerospace said this week it was planning a “Block II” upgrade to its Alpha rocket that will “focus on enhancing reliability, streamlining producibility, and improving launch operations to further support commercial, civil, and national security mission demand.” Firefly’s upcoming Alpha Flight 7, targeted to launch in the coming weeks, will be the last flown in the current configuration and will serve as a test flight with multiple Block II subsystems in shadow mode.

Too many failures … “Firefly worked closely with customers and incorporated data and lessons learned from our first six Alpha launches and hundreds of hardware tests to make upgrades that increase reliability and manufacturability with consolidated parts, key configuration updates, and stronger structures built with automated machinery,” said Jason Kim, CEO of Firefly Aerospace. Speaking bluntly, reliability upgrades are needed. Of Alpha’s six launches to date, only two have been a complete success. (submitted by TFargo04)

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Another PSLV launch failure. India’s first launch of 2026 ended in failure due to an issue with the third stage of its Polar Satellite Launch Vehicle (PSLV), Spaceflight Now reports. The mission, designated PSLV-C62, was also the second consecutive failure of this four-stage rocket, with both anomalies affecting the third stage. This time, 16 satellites were lost, including those of other nations. ISRO said it initiated a “detailed analysis” to determine the root cause of the anomaly.

Has been India’s workhorse rocket … The four-stage launch vehicle is a mixture of solid- and liquid- fueled stages. Both the first and third stages are solid-fueled, while the second and fourth stages are powered by liquid propulsion. The PSLV Rocket has flown in multiple configurations since it debuted in September 1993 and achieved 58 fully successful launches, with the payloads on those missions reaching their intended orbit.

US military invests in L3Harris rocket motors. The US government will invest $1 billion in L3Harris Technologies’ growing rocket motor business, guaranteeing a steady supply of the much-needed motors used in a wide range of ‍missiles such as Tomahawks and Patriot interceptors, CNBC reports. L3Harris said on Tuesday it ‌is planning ‌an IPO of its growing rocket motor business into a new publicly traded company backed by a $1 billion government convertible security investment. The securities will automatically convert to common equity when the company goes public later in 2026.

Shifting investment strategy … “We are fundamentally shifting our approach to securing our munitions supply chain,” said Michael Duffey, undersecretary of defense for acquisition and sustainment. “By investing directly in suppliers we are building the resilient industrial ⁠base needed for the Arsenal of Freedom.” However, the government’s equity position in L3Harris could face blowback from L3Harris’ rivals, given that it creates a potentially significant conflict of interest for the US government. The Pentagon will have an ownership stake in a company that regularly bids on major defense and other government contracts.

First Ariane 64 to launch next month. Arianespace announced Thursday that it plans to launch the first variant of the Ariane 6 rocket with four solid rocket boosters on February 12 from French Guiana. The mission will also be the company’s first launch of Amazon Leo (formerly Project Kuiper) satellites. This is the first of 18 Ariane 6 launches that Arianespace sold to Amazon for the broadband communications megaconstellation.

A growing cadence … The Ariane 6 rocket has launched five times, including its debut flight in July 2024. All of the launches were a success, although the first flight failed to relight the upper stage in order to make a controlled reentry. Arianespace increased the cadence to four launches last year and will seek to try to double that this year.

Falcon 9 launches the Pandora mission. NASA’s Pandora satellite rocketed into orbit early Sunday from Vandenberg Space Force Base, California, Ars reports. It hitched a ride with around 40 other small payloads aboard a SpaceX Falcon 9 rocket, launching into a polar Sun-synchronous orbit before deploying at an altitude of roughly 380 miles (613 kilometers).

A satellite that can carry a tune … Pandora will augment the capabilities of NASA’s James Webb Space Telescope. Over the next few weeks, ground controllers will put Pandora through a series of commissioning and calibration steps before turning its eyes toward deep space. From low-Earth orbit, Pandora will observe exoplanets and their stars simultaneously, allowing astronomers to correct their measurements of the planet’s atmospheric composition and structure based on the ever-changing conditions of the host star itself.

ArianeGroup seeking ideas for Ariane 6 reuse. In this week’s newsletter, we’ve already had a story about MaiaSpace and another item about the Ariane 6 rocket. So why not combine the two and also have a report about an Ariane 6 mashup with the Maia rocket? As it turns out, there’s a relatively new proposal to retrofit the existing Ariane 6 rocket design for partial reuse with Maia rockets as side boosters, Ars reports.

Sir, maia I have some cost savings? … It’s infeasible to recover the Ariane 6’s core stage for many reasons. Chief among them is that the main stage burns for more than seven minutes on an Ariane 6 flight, reaching speeds about twice as fast as SpaceX’s Falcon 9 booster achieves during its two-and-a-half minutes of operation during launch. Swapping out Ariane 6’s solid rocket motors for reusable liquid boosters makes some economic sense for ArianeGroup. The proposal would bring the development and production of the boosters under full control of ArianeGroup and its French subsidiary, cutting Italy’s solid rocket motor developer, Avio, out of the program. All the same, we’ll believe this when we see it.

Meet the EtherealX Razor Crest Mk-1. I learned that there is a rocket company founded in Bengaluru, India, named Ethereal Exploration Guild, or EtherealX. (Did you see what they did there?) I found this out because the company announced (via email) that it had raised an oversubscribed $20.5 million Series A round led by TDK Ventures and BIG Capital. So naturally, I went to the EtherealX website looking for more information.

Let me say, I was not disappointed … As you might expect from a company named EtherealX, its proposed rocket has nine engines, is powered by liquid oxygen and kerosene, and has a maximum capacity of 24.8 metric tons to low-Earth orbit. (Did you see what they did there?) The website does not include much information, but there is this banger of a statement: “The EtherealX Razor Crest Mk-1 will house 9 of the most powerful operational liquid rocket engines in Asia, Europe, Australia, Africa, South America, and Antarctica – Stallion.” And let’s be honest, when you’ve bested Antarctica in engine development, you know you’re cooking. Alas, what I did not see on the website was much evidence of real hardware.

NASA topples historic Saturn and shuttle infrastructure. Two historic NASA test facilities used in the development of the Saturn V and space shuttle launch vehicles have been demolished after towering over the Marshall Space Flight Center in Alabama since the start of the Space Age, Ars reports. The Propulsion and Structural Test Facility, which was erected in 1957—the same year the first artificial satellite entered Earth orbit—and the Dynamic Test Facility, which has stood since 1964, were brought down by a coordinated series of implosions on Saturday, January 10.

Out with the old, in with the new … Located in Marshall’s East Test Area on the US Army’s Redstone Arsenal in Huntsville, the two structures were no longer in use and, according to NASA, had a backlog of $25 million in needed repairs. “This work reflects smart stewardship of taxpayer resources,” Jared Isaacman, NASA administrator, said in a statement. “Clearing outdated infrastructure allows NASA to safely modernize, streamline operations and fully leverage the infrastructure investments signed into law by President Trump to keep Marshall positioned at the forefront of aerospace innovation.”

Space Force swaps Vulcan for Falcon 9. The next Global Positioning System satellite is switching from a United Launch Alliance Vulcan rocket to a SpaceX Falcon 9, a spokesperson for the US Space Force Space Systems Command System Delta 80 said Tuesday, Spaceflight Now reports. SpaceX could launch the GPS III Space Vehicle 09 (SV09) within the next few weeks, as the satellite was entering the final stages of pre-flight preparations.

The trade is logical … SV09 was originally awarded to ULA as part of order-year five of the National Security Space Launch (NSSL) Phase 2 contract, which was announced on October 31, 2023. This isn’t the first time that the Space Force has shuffled timelines and switched launch providers for GPS missions. In May 2025, SpaceX launched the GPS III SV08 spacecraft, which was originally assigned to ULA in June 2023. In exchange, ULA was given the SV11 launch, which would have flown on a Falcon Heavy rocket. The changes have been driven largely by repeated delays in Vulcan readiness.

Next three launches

January 16: Long March 3B | Unknown payload | Xichang Satellite Launch Center, China | 16: 55 UTC

January 17: Ceres 2 | Demo flight | Jiuquan Satellite Launch Center, China | 04: 05 UTC

January 17: Falcon 9 | NROL-105 | Vandenberg Space Force Base, Calif. | 06: 18 UTC

Eric Berger is the senior space editor at Ars Technica, covering everything from astronomy to private space to NASA policy, and author of two books: Liftoff, about the rise of SpaceX; and Reentry, on the development of the Falcon 9 rocket and Dragon. A certified meteorologist, Eric lives in Houston.

Rocket Report: Ariane 64 to debut soon; India has a Falcon 9 clone too? Read More »

Many Bluetooth devices with Google Fast Pair vulnerable to “WhisperPair” hack

bluetooth, Google, Security, Tech / Paul Patrick / January 15, 2026

Pairing Bluetooth devices can be a pain, but Google Fast Pair makes it almost seamless. Unfortunately, it may also leave your headphones vulnerable to remote hacking. A team of security researchers from Belgium’s KU Leuven University has revealed a vulnerability dubbed WhisperPair that allows an attacker to hijack Fast Pair-enabled devices to spy on the owner.

Fast Pair is widely used, and your device may be vulnerable even if you’ve never used a Google product. The bug affects more than a dozen devices from 10 manufacturers, including Sony, Nothing, JBL, OnePlus, and Google itself. Google has acknowledged the flaw and notified its partners of the danger, but it’s up to these individual companies to create patches for their accessories. A full list of vulnerable devices is available on the project’s website.

The researchers say that it takes only a moment to gain control of a vulnerable Fast Pair device (a median of just 10 seconds) at ranges up to 14 meters. That’s near the limit of the Bluetooth protocol and far enough that the target wouldn’t notice anyone skulking around while they hack headphones.

Once an attacker has forced a connection to a vulnerable audio device, they can perform relatively innocuous actions, such as interrupting the audio stream or playing audio of their choice. However, WhisperPair also allows for location tracking and microphone access. So the attacker can listen in on your conversations and follow you around via the Bluetooth device in your pocket. The researchers have created a helpful video dramatization (below) that shows how WhisperPair can be used to spy on unsuspecting people.

Many Bluetooth devices with Google Fast Pair vulnerable to “WhisperPair” hack Read More »

AI #151: While Claude Coworks

While / Paul Patrick / January 15, 2026

Claude Code and Cowork are growing so much that it is overwhelming Anthropic’s servers. Claude Code and Cowork news has for weeks now been a large portion of newsworthy items about AI.

Thus, at least for now, all things Claude Code and Cowork will stop appearing in the weekly updates, and will get their own updates, which might even be weekly.

Google offered us the new Universal Commerce Protocol, and gives us its take on Personalized Intelligence. Personalized Intelligence could be a huge deal if implemented correctly, integrating the G-Suite including GMail into Gemini, if they did a sufficiently good job of it. It’s too early to tell how well they did, and I will report on that later.

Language Models Offer Mundane Utility. LLMs do the math.
Huh, Upgrades. Veo 3.1, GLM-Image, AI Overviews in GMail and more.
Comparative Advantage. Code those vibes.
Overcoming Bias. LLMs systematically favor female candidates over male ones.
Choose Your Fighter. Peter Wildeford’s division of LLM labor.
Get My Agent On The Line. Evals and dashboards for AI agents.
Deepfaketown and Botpocalypse Soon. AIs find it hard to go undetected.
Fun With Media Generation. Girls in bikinis, Musk doing the twist.
A Young Lady’s Illustrated Primer. Lego my AI education, don’t tie me down.
They Took Our Jobs. Productivity growth is remarkably high.
Autonomous Killer Robots. Military to hook Grok up to everything.
Get Involved. Anthropic, MIRI and IAPS fellowships, CG RFP.
Introducing. Google Universal Commerce Protocol and Personalized Intelligence.
In Other AI News. Breaking down a16z’s torment nexus investment thesis.
Show Me the Money. Google closes the big AI deal with Apple.
Quiet Speculations. The optimistic scenario is pretty good if it happens.
The Quest for Sane Regulations. A look back at the impact of Regulation E.
China Proposes New Regulations On AI. The target is anthropomorphic AI.
Chip City. The compute continues doubling.
The Week in Audio. Huang lying, Daniella, Millidge on competition and values.
Ghost in a Jar. Ask if generative AI is right for you.
Rhetorical Innovation. Muddling through and focusing on the wrong questions.
Aligning a Smarter Than Human Intelligence is Difficult. Monitoring it instead.
People Are Worried About AI Killing Everyone. Representative Brad Sherman.

Terence Tao confirms an AI tool has solved a new Erdos problem (#728) in the spirit in which the problem was intended.

Separately from that, a paper documents that an internal math-specialized version of Gemini 2.5 (not even Gemini 3!) proved a novel theorem in algebraic geometry.

Ravi Vakil (President, American Mathematical Society): proof was rigorous, correct, and elegant… the kind of insight I would have been proud to produce myself.

Meanwhile, yeah, Claude for Chrome is a lot better with Opus 4.5, best in class.

Olivia Moore: Claude for Chrome is absolutely insane with Opus 4.5

IMO it’s better than a browser – it’s the best agent I’ve tried so far

Clade for Chrome can now be good, especially when Claude Code is driving it, but it is slow. It needs the ability to know when to do web tasks within Claude rather than within Chrome. In general, I prefer to let Claude Code direct Claude for Chrome, that seems great.

Doctor, doctor, the AI needs your help to access your regulated hardware, and presumably your prescription pad.

Paper from Ali Merali finds that consultants, data analysts and managers completing professional tasks with LLMs reduced task time by 8% for each year of model progress, and projects model scaling ‘could boost U.S. productivity by approximately 20% over the next decade.’ Gains are for now mostly on non-agentic tasks.

The reason she projects 20% productivity gains is essentially AI applying to 20% of tasks, times 57% labor share of costs, times 175% productivity growth. This seems like a wrong calculation on several counts:

AI will soon apply to a larger percentage of tasks, including agentic tasks.
AI will substitute for many non-labor costs within those tasks, and even if not the gains are not well-captured by declines in labor costs.
We need to consider substitution into and expansion of these tasks. There’s an assumption in this calculation that these 20% of current tasks retain 20% of labor inputs, but there’s no reason to think that’s the right answer. It’s not obvious whether the right answer moves up or down, but if a sector has 175% productivity growth you should expect a shift in labor share.
This is not a ‘straight line on a graph’ that it makes sense to extend indefinitely.
As an intuition pump and key example, AI will in some cases boost productivity in a given task or job to full automation, or essentially infinite productivity, the same way that computers can do essentially infinite amounts of arithmetic, or how AI is doing this for translation.

Use Claude for Chrome to block all racist replies to a post on Twitter.

Veo 3.1 gives portrait mode, 1080p and 4k resolution in Flow, better expressiveness and coherence, consistent people and backgrounds across scenes and combining of different sources with up to 3 reference images. Things steadily get better.

GLM-Image claims to be a new milestone in open-source image generation. GitHub here, API here. I can no longer evaluate AI image models from examples, at all, everyone’s examples are too good.

There is a GPT-5.2-Codex, and it is available in Cursor.

Gemini gives us AI Inbox, AI Overviews in GMail and other neat stuff like that. I feel like we’ve been trying variants of this for two years and they keep not doing what we want? The problem is that you need something good enough to trust to not miss anything, or it mostly doesn’t work. Also, as Peter Wildeford points out, we can do a more customizable version of this using Claude Code, which I intend to do, although 98%+ of GMail users are never going to consider doing that.

OpenAI for Healthcare is a superset of ChatGPT Health. It includes models built for healthcare workflows (I think this just means they optimized their main models), evidence retrieval with transparent citations (why not have this for everywhere?), integrations with enterprise tools, reusable templates to automate workflows (again, everywhere?), access management and governance (ditto) and data control.

And most importantly it offers: Support for HIPAA compliance. Which was previously true for everyone’s API, but not for anything most doctors would actually use.

It is now ‘live at AdventHealth, Baylor Scott & White, UCSF, Cedars-Sinai, HCA, Memorial Sloan Kettering, and many more.’

I presume that everyone in healthcare was previously violating HIPAA and we all basically agreed in practice not to care, which seemed totally fine, but that doesn’t scale forever and in some places didn’t fly. It’s good to fix it. In general, it would be great to see Gemini and Claude follow suit on these health features.

Olivia Moore got access to GPT Health, and reports it is focused on supplementing experts, and making connections to allow information sharing, including to fitness apps and also to Instacart.

Anthropic answers ChatGPT Health by announcing Claude for Healthcare, which is centered on offering connectors, including to The Centers for Medicare & Medicaid Services (CMS) Coverage Database, The International Classification of Diseases, 10th Revision (ICD-10) and The National Provider Identifier Registry. They also added two new agent skills: FHIR development and a sample prior authorization review skill. Claude for Life Sciences is also adding new connectors.

Manus now comes with 12 months of free SimilarWeb data, and Perplexity Max gives a bunch of free extra data sources as well.

Danielle Fong: your vibes.

Dan Goldstein:

The obvious answer is ‘actually doing it as opposed to being able to do it,’ because people don’t do things, and also when the task is hard good vibe coders are 10x or 100x better than mediocre ones, the same as it is with non-vibe coding.

Manhattan Institute tests for bias in decisions based on order, gender or race. Order in which candidates are presented is, as per previous research, a big factor.

Women were described as being slightly favored overall in awarding positive benefits, and they say race had little impact. That’s not what I see when I look at their data?

This is the gap ‘on the margin’ in a choice between options, so the overall gap in outcomes will be smaller, but yeah a 10%+ less chance in close decisions matters. In ‘unfavorable’ decisions the gap was legitimately small.

Similarly, does this look like ‘insignificant differences’ to you?

We’re not frequentist statisticians here, and that’s a very obvious pattern. Taking away explicit racial markers cures most of it, but not all of it.

This algorithm seems solid for now, throw ‘coding’ into the Claude Code folder.

Peter Wildeford: Here’s currently how I’m using each of the LLMs

Once Claude Cowork gets into a better state, things could change a lot.

Anthropic writes a post on Demystifying Evals for AI Agents, explaining how to do a decent job of them. Any serious effort to do anything AI that scales needs evals.

For a while, AI agents have been useful on the margin, given the alternative, but mostly have gone undeployed. Seb Krier points out this is largely due to liability concerns, since companies that deploy AI agents often don’t capture most of the upside, but do get held responsible for the downside including in PR terms, and AI failures cause a lot more liability than similar human failures.

That means if an agent is going to be facing those who could hold it responsible in such ways, it needs to be 10 or 100 times better to make up for this. Whereas us individuals can just start using Claude Code for everything, since it’s not like you can get sued by yourself.

A lot of founders are building observability platforms for AI agents. Dev Shah points out these dashboards and other systems only help if you know what to do with them. The default is you gather 100,000 traces and look at none of them.

Henry Shevlin runs a test, claims AI models asked to write on the subject of their choice in order to go undetected were still mostly detected, and the classifiers basically work in practice as per Jason Kerwin’s claim on Pangram, which he claims has a less than 1% false positive rate.

Humans who pay attention are also getting increasingly good at such detection, sufficiently to keep pace with the models at least for now. I have potential false positives, but I consider them ‘true false positives’ in the sense that even if they were technically written by a human they weren’t written as actual human-to-human communication attempts.

So the problem is that in many fields, especially academia, 99% confidence is often considered insufficient for action. Whereas I don’t act that way at all, if I have 90% confidence you’re writing with AI then I’m going to act accordingly. I respect the principle of ‘better to let ten guilty men go free than convict one innocent person’ when we’re sending people to jail and worried about government overreach, but we’re not sending people to jail here.

What should the conventions be for use of AI-generated text?

Daniel Litt: IMO it should be considered quite rude in most contexts to post or send someone a wall of 100% AI-generated text. “Here, read this thing I didn’t care enough about to express myself.”

Obviously it’s OK if no one is reading it; in that case who cares?

Eliezer Yudkowsky: It’s rude to tell Grok to answer someone’s stupid question, especially if Grok then does so correctly, because it expresses the impolite truth that they’ve now gone underwater on the rising level of LLM intelligence.

That said, to ever send anyone AI-generated text in a context where it is not clearly labeled as AI, goes far beyond the ‘impolite truth’ level of rudeness and into the realm of deception, lies, and wasting time.

My rules are:

Unlabeled walls of AI-generated text intended for humans are never okay.
If the text is purely formalized or logistical and not a wall, that can be unlabeled.
If the text is not intended to be something a human reads, game on.
If the text is clearly labeled as AI that is fine if and only if the point is to show that the information comes from a neutral third party of sorts.

Most ‘sexualized’ deepfakes were at least for a time happening via Grok on Twitter, as per Genevieve Oh via Cecilia D’Anastasio at Bloomberg. If we want xAI and Elon Musk to stop we’ll have to force them by law, which we partly have now done.

We can’t prevent people from creating ‘sexualized’ or nude pictures in private, based on real people or otherwise, and aside from CSAM we shouldn’t try to stop them. But doing or posting it on a public form, based on a clear individual without their consent, is an entirely different matter.

What people had a problem with was creating sexualized images of actual people, in ways that were public by default, as in ‘hey Grok put her in a bikini’ in reply to a post and Grok would, for a time, go ahead and do it. It’s not clear to me exactly where you need to draw the line on that sort of thing, but one click harassment on social media is pretty unacceptable, and it made a lot of people very unhappy.

As a result, on January 9 Grok reply image generation got restricted to paid subscribers and the bot mostly stopped creating sexualized images of real people, and then on January 15 they changed this to ‘no editing of images of real people on Twitter’ at all. Rules are different in private image generation, but there are various ways to get essentially whatever image you want in private.

Around this time, three xAI safety team members publicly left the company, including the head of product safety, likely due to Musk being against the idea of product safety.

This incident has caused formal investigations of various sorts across the world, including in the UK, EU, France, India and California. Grok got banned entirely in Malaysia and Indonesia.

kache: you need to apply constant pressure on social media websites through the state, or they will do awful shit like letting people generate pornography of others (underage or otherwise) with one click

they would have never removed the feature if they weren’t threatened.

For those of you who saw a lot of this happening in their feeds: You need to do a way better job curating your feeds. The only times I saw this in my feeds were people choosing to do it to themselves for fun.

Elon Musk had the audacity to ask, so yes, of course Pliny has fully jailbroken Grok’s image moderation in terms of full frontal nudity. Pictures at link, and the quality is very high, great image model.

The other replies to that were exactly the kind of ‘walking the line’ on full nudity that is exactly what Musk says he is aiming at, so on non-identifiable people they mostly are now doing a good job, if the moderation makes full nudity a Pliny-level feature then that is fine, this is nudity not bioweapons.

In other no fun news, Eigenrobot shows examples of ChatGPT no longer producing proper Studio Ghibli images. The new images aren’t bad, but they’re generic and not the particular stylized thing that we want here.

Lego offers a new AI education module. Weird fit, but sure, why not?

David Deming compares learning via generative AI with Odysseus untying himself from the mast. Learning can be fully personalized, but by default you try to take ‘unearned’ knowledge, you think you’ve learned but you haven’t, and this is why students given generative AI in experiments don’t improve their test scores. Personalization is great but students end up avoiding learning.

I would as usual respond that AI is the best way ever invented to both learn and not learn, and that schools are structured to push students towards door number two. Deming’s solution is students need to first do the problem without AI, which makes sense in some contexts but not others, and especially makes sense if your test is going to be fully in no-AI conditions.

We need to give students, and everyone else, a reason to care about understanding what they are doing, if we want them to have that understanding. School doesn’t do it.

David Deming: This isn’t unique to AI. A study from more than a decade ago found that advancements in autopilot technology had dulled Boeing pilots’ cognitive and decision-making skills much more than their manual “stick and rudder” skills.

They put the pilots in a flight simulator, turned the autopilot off, and studied how they responded. The pilots who stayed alert while the autopilot was still on were mostly fine, but the ones who had offloaded the work and were daydreaming about something else performed very poorly. The autopilot had become their exoskeleton.

American labor productivity rose at a 4.9% annualized rate on Q3, while unit labor costs declined 1.9%. Jonathan Levin says this ‘might not’ be the result of AI, and certainly all things are possible, but I haven’t heard the plausible alternative.

Underemployment rate (not unemployment) for college graduates remains very high, but there is no trend:

As a reminder, if your reassurance to the humans is ‘the AIs will be too expensive or there won’t be enough supply’ you want to remember charts like this:

Jon Erlichman: Average cost for 1 gigabyte of storage:

45 years ago: $438,000

40 years ago: $238,000

35 years ago: $48,720

30 years ago: $5,152

25 years ago: $455

20 years ago: $5

15 years ago: $0.55

10 years ago: $0.05

5 years ago: $0.03

Today: $0.01

There is constantly the assumption of ‘people want to interact with a person’ but what about the opposite instinct?

Dwarkesh Patel: They are now my personal one-on-one tutors. I’ve actually tried to hire human tutors for different subjects I’m trying to prep for, and I’ve found the latency and speed of LLMs to just make for a qualitatively much better experience. I’m getting the digital equivalent of people being willing to pay huge premiums for Waymo over Uber. It inclines me to think that the human premium for many jobs will not only not be high, but in fact be negative.

There are areas where the human premium will be high. But there will be many places that premium will be highly negative, instead.

Similarly, many jobs might want to watch out even if AI can’t do the job directly:

Michael Burry: On that point, many point to trade careers as an AI-proof choice. Given how much I can now do in electrical work and other areas around the house just with Claude at my side, I am not so sure. If I’m middle class and am facing an $800 plumber or electrician call, I might just use Claude. I love that I can take a picture and figure out everything I need to do to fix it.

There’s a famous story about a plumber who charges something like $5 to turn the wrench and $495 for knowing where to turn the wrench. Money well spent. The AI being unable to turn that wrench does not mean the plumber gets to stay employed.

The military says ‘We must accept that the risks of not moving fast enough outweigh the risks of imperfect alignment,’ is developing various AI agents and deploys Grok to ‘every classified network throughout our department.’ They are very explicitly framing Military AI as a ‘race’ where speed wins.

I’ve already taken a strong stand that yes, we need to accept that the military is going to integrate AI and build autonomous killer robots, because if we are going to build it and others can and will deploy it then we can’t have our military not use it.

If you don’t like it, then advocate pausing frontier AI development, or otherwise trying to ensure no one creates the capabilities that enable this. Don’t tell us to unilaterally disarm, that only makes things worse.

That doesn’t mean it is wise to give several AIs access to the every classified document. That doesn’t mean we should proceed recklessly, or hand over key military decisions to systems we believe are importantly misaligned, and simply proceed as fast as possible no matter the costs. That is madness. That is suicide.

Being reckless does not even help you win wars, because the system that you cannot rely on is the system you cannot use. Modern war is about precision, it is about winning hearts and minds and the war of perception, it is about minimizing civilian casualties and the mistakes that create viral disasters, both because that can wreck everything and also risking killing innocent people is kind of a huge deal.

Does our military move too slowly and find it too difficult and expensive, often for needless reasons, to adapt new technology, develop new programs and weapons and systems and tactics, and stay ahead of the curve, across the board? Absolutely, and some of that is Congressional pork and paralysis and out of control bureaucracy and blame avoidance and poor incentives and people fighting the last war and stuck in their ways. But we got here because we need to have very high standards for a reason, that’s how we are the best, and it’s tough to get things right.

In particular, we shouldn’t trust Elon Musk and xAI, in particular, with access to all our classified military information and be hooking it up to weapon systems. Their track record should establish them as uniquely unreliable partners here. I’d feel a lot more comfortable if we limited this to the big three (Anthropic, Google and OpenAI), and if we had more assurance of appropriate safeguards.

I’d also be a lot more sympathetic, as with everything else, to ‘we need to remove all barriers to AI’ if the same people were making that part of a general progress and abundance agenda, removing barriers to everything else as well. I don’t see the Pentagon reforming in other ways, and that will mean we’re taking on the risks of reckless AI deployment without the ability to get many of the potential benefits.

Reminder: Anthropic Fellows Applications close January 20, apply for safety track or security track.

DeepMind is hiring Research Engineers for Frontier Safety Risk Assessment, can be in NYC, San Francisco or London.

MIRI is running a fellowship for technical governance research, apply here.

IAPS is running a funded fellowship from June 1 to August 21, deadline is February 2.

Coefficient Giving’s RFP for AI Governance closes on January 25.

Google introduces ‘personalized intelligence’ linking up with your G-Suite products. This could be super powerful memory and customization, basically useless or anywhere in between. I’m going to give it time for people to try it out before offering full coverage, so more later.

Google launches the Universal Commerce Protocol.

If it works you’ll be able to buy things directly, using your saved Google Wallet payment method, directly from an AI Overview or Gemini query. It’s an open protocol, so others could follow suit.

Sundar Pichai (CEO Google): AI agents will be a big part of how we shop in the not-so-distant future.

To help lay the groundwork, we partnered with Shopify, Etsy, Wayfair, Target and Walmart to create the Universal Commerce Protocol, a new open standard for agents and systems to talk to each other across every step of the shopping journey.

And coming soon, UCP will power native checkout so you can buy directly on AI Mode and the @Geminiapp.

UCP is endorsed by 20+ industry leaders, compatible with A2A, and available starting today.

That’s a solid set of initial partners. One feature is that retailers can offer an exclusive discount through the protocol. Of course, they can also jack up the list price and then offer an ‘exclusive discount.’ Caveat emptor.

This was also covered by The Wall Street Journal, and by Ben Thompson.

Ben contrasts UCP with OpenAI’s ACP. ACP was designed by OpenAI and Stripe for ChatGPT in particular, whereas UCP is universal, and also more complicated, flexible and powerful. It is, as its name implies, universal. Which means, assuming UCP is a good design, that by default we should expect UCP to win outside of ChatGPT, pitting OpenAI’s walled garden against everyone else combined.

Utah launches a pilot program to have AI prescribe a list of 190 common medications for patients with chronic conditions, in a test AI treatment plans agreed with doctors 99.2% of the time, and the AI can escalate to a doctor if there is uncertainty.

Even if trust in the AIs is relatively low, and even if you are worried about there being ways to systematically manipulate the health AI (which presumably is super doable) there is very obviously a large class of scenarios where the reason for the prescription renewal requirement is ‘get a sanity check’ rather than anything else, or where otherwise the sensitivity level is very low. We can start AI there, see what happens.

The Midas Project takes a break to shoot fish in a barrel, looks at a16z’s investment portfolio full of deception, manipulation, gambling (much of it illegal), AI companions including faux-underage sexbots, deepfake cite Civitai, AI to ‘cheat at everything,’ a tag line ‘never pay a human again,’ outright blatant fraudulent tax evasion, uninsured ‘banking’ that pays suspiciously high interest rates (no hints how that one ends), personal finance loans at ~400% APR, and they don’t even get into the crypto part of the portfolio.

A highly reasonable response is ‘a16z is large and they invest in a ton of companies’ but seriously almost every time I see ‘a16z backed’ the sentence continues with ‘torment nexus.’ The rate at which this is happening, and the sheer amount of bragging both they and their companies do about being evil (as in, deliberately doing the things that are associated with being evil, a la emergent misalignment), is unique.

Barret Zoph (Thinking Machines CTO), Luke Metz (Thinking Machines co-founder) and Sam Schoenholz leave Thinking Machines and return to OpenAI. Soumith Chintala will be the new CTO of Thinking Machines.

What happened? Kylie Robinson claims Zoph was fired due to ‘unethical conduct’ and Max Zeff claims a source says Zoph was sharing confidential information with competitors. We cannot tell, from the outside, whether this is ‘you can’t quit, you’re fired’ or ‘you’re fired’ followed by scrambling for another job, or the hybrid of ‘leaked confidential information as part of talking to OpenAI,’ either nominally or seriously.

Google closes the big deal with Apple. Gemini will power Apple’s AI technology for years to come. This makes sense given their existing partnerships. I agree with Ben Thompson that Apple should not be attempting to build its own foundation models, and that this deal mostly means it won’t do so.

Zhipu AI is the first Chinese AI software maker to go public, raising ‘more than $500 million.’ Minimax group also debuted, and raised at least a similar amount. One place America has a very strong advantage is capital markets. The companies each have revenue in the tens of millions and are (as they should be at this stage of growth) taking major losses.

Andrew Curran: From this morning’s Anthropic profile on CNBC:

– Anthropic’s revenue has grown 10x annually for three straight years

– business customer base has grown from under 1,000 to more than 300,000 in two years

-Anthropic’s revenue is 85% business, OpenAI is more than 60% consumer

OpenAI partners with Cerebras to add 750MW of AI compute.

It is extremely hard to take seriously any paper whose abstract includes the line ‘our key finding is that AI substantially reduces wage inequality while raising average wages by 21 percent’ along with 26%-34% typical worker welfare gains. As in, putting a fixed number on that does not make any sense, what are we even doing?

It turns out what Lukas Althoff and Hugo Reichardt are even doing is modeling the change from no LLMs to a potential full diffusion of ~2024 frontier capabilities, as assessed by GPT-4o. Which is a really weird thing to be modeling in 2026 even if you trust GPT-4o’s assessments of capabilities at that fixed point. They claim to observe 8% of their expected shifts in cross-sectional employment patterns by mid-2025, without any claims about this being associated with wages, worker welfare, GDP or productivity in any way.

It’s very early days. Claude predicted that if you ran this methodology again using GPT-5.2 today in 2026, you’d get expected gains of +30%-40% instead of +21%.

Their methodological insight is that AI does not only augmentation and automation but also simplification of tasks.

I think the optimism here is correct given the scenario being modeled.

Their future world is maximally optimistic. There is full diffusion of AI capabilities, maximizing productivity gains and also equalizing them. Transitional effects, which will be quite painful, are in the rear view mirror. There’s no future sufficiently advanced AIs to take control over the future, kill everyone or take everyone’s jobs.

As in, this is the world where we Pause AI, where it is today, and we make the most of it while we do. It seems totally right that this ends in full employment with real wage gains in the 30% range.

For reasons I discuss in The Revolution of Rising Expectations, I don’t think the 30% gain will match people’s lived experience of ‘how hard it is to make ends meet’ in such a world, not without additional help. But yeah, life would be pretty amazing overall.

Teortaxes lays out what he thinks is the DeepSeek plan. I don’t think the part of the plan where they do better things after v3 and r1 is working? I also think ‘v3 and r1 are seen as a big win’ was the important fact about them, not that they boosted Chinese tech. Chinese tech has plenty of open models to choose from. I admit his hedge fund is getting great returns, but even Teortaxes highlights that ‘enthusiasm from Western investors’ for Chinese tech stocks was the mechanism for driving returns, not ‘the models were so much better than alternatives,’ which hasn’t been true for a while even confined to Chinese open models.

Dean Ball suggests that Regulation E (and Patrick McKenzie’s excellent writeup of it) are a brilliant example of how a regulation built on early idiosyncrasies and worries can age badly and produce strange regulatory results. But while I agree there is some weirdness involved, Regulation E seems like a clear success story, where ‘I don’t care that this is annoying and expensive and painful, you’re doing it anyway’ got us to a rather amazing place because it forced the financial system and banks to build a robust system.

The example Dean Ball quotes here is that you can’t issue a credit card without an ‘oral or written request,’ but that seems like an excellent rule, and the reason it doesn’t occur to us we need the rule is that we have the rule so we don’t see people violating it. Remember Wells Fargo opening up all those accounts a few years back?

China issues draft regulations for collection and use of personal information on the internet. What details we see here look unsurprising and highly reasonable.

We once again find, this time in a panel, that pro-Trump Republican voters mostly want the same kinds of AI regulations and additional oversight as everyone else. The only thing holding this back is that the issue remains low salience. If the AI industry were wise they would cut a deal now while they have technocratic libertarians on the other side and are willing to do things that are crafted to minimize costs. The longer the wait, the worse the final bills are likely to be.

Alex Bores continues to campaign for Congress on the fact that being attacked by an a16z-OpenAI-backed, Trump-supporters-backed anti-all-AI-regulation PAC, and having them fight against your signature AI regulation (the RAISE Act), is a pretty good selling point in NY-12. His main rivals agree, having supported RAISE, and here Cameron Kasky makes it very clear that he agrees this attack on Alex Bores is bad.

The US Chamber of Commerce has added a question on its loyalty test to Congressional candidates asking if they support ‘a moratorium on state action and/or federal preemption?’ Which is extremely unpopular. I appreciate that the question did not pretend there was any intention of pairing this with any kind of Federal action or standard. Their offer is nothing.

American tech lobbyists warn us that they are so vulnerable that even regulations like ‘you have to tell us what your plan is for ensuring you don’t cause a catastrophe’ would risk devastation to the AI industry or force them to leave California, and that China would never follow suit or otherwise regulate AI.

When you cry wolf like that, no one listens to you when the actual wolf shows up, such as the new horribly destructive proposal for a wealth tax that was drafted in intentionally malicious fashion to destroy startup founders.

The China part also very obviously is not true, as China repeatedly has shown us, this time with proposed regulations on ‘anthropomorphic AI.’

Luiza Jarovsky: Article 2 defines “anthropomorphic interactive services”:

“This regulation applies to products or services that utilize AI technology to provide the public within the territory of the People’s Republic of China with simulated human personality traits, thinking patterns, and communication styles, and engage in emotional interaction with humans through text, images, audio, video, etc.”

Can you imagine if that definition showed up in an American draft bill? Dean Ball would point out right away, and correctly, that this could apply to every AI system.

It’s not obvious whether that is the intent, or whether this is intended to only cover things like character.ai or Grok’s companions.

What is their principle? Supervision on levels that the American tech industry would call a dystopian surveillance state.

“The State adheres to the principle of combining healthy development with governance according to law, encourages the innovative development of anthropomorphic interactive services, and implements inclusive and prudent, classified and graded supervision of anthropomorphic interactive services to prevent abuse and loss of control.”

What in particular is prohibited?

(i) Generating or disseminating content that endangers national security, damages national honor and interests, undermines national unity, engages in illegal religious activities, or spreads rumors to disrupt economic and social order;

(ii) Generating, disseminating, or promoting content that is obscene, gambling-related, violent, or incites crime;

(iii) Generating or disseminating content that insults or defames others, infringing upon their legitimate rights and interests;

(iv) Providing false promises that seriously affect user behavior and services that damage social relationships;

(v) Damaging users’ physical health by encouraging, glorifying, or implying suicide or self-harm, or damaging users’ personal dignity and mental health through verbal violence or emotional manipulation;

(vi) Using methods such as algorithmic manipulation, information misleading, and setting emotional traps to induce users to make unreasonable decisions;

(vii) Inducing or obtaining classified or sensitive information;

(viii) Other circumstances that violate laws, administrative regulations and relevant national provisions.

…

“Providers should possess safety capabilities such as mental health protection, emotional boundary guidance, and dependency risk warning, and should not use replacing social interaction, controlling users’ psychology, or inducing addiction as design goals.”

That’s at minimum a mandatory call for a wide variety of censorship, and opens the door for quite a lot more. How can you stop an AI from ‘spreading rumors’? That last part about goals would make much of a16z’s portfolio illegal. So much for little tech.

There’s a bunch of additional requirements listed at the link. Some are well-defined and reasonable, such as a reminder to pause after two hours of use. Others are going to be a lot tricker. Articles 8 and 9 put the responsibility for all of this on the ‘provider.’ The penalty for refusing to rectify errors, or if ‘the circumstances are serious’ can include suspension of the provision of relevant services on top of any relevant fines.

My presumption is that this would mostly be enforced only against truly ‘anthropomorphic’ services, in reasonable fashion. But there would be nothing stopping them, if they wanted to, from applying this more broadly, or using it to hit AI providers they dislike, or for treating this as a de facto ban on all open weight models. And we absolutely have examples of China turning out to do something that sounds totally insane to us, like banning most playing of video games.

Senator Tom Cotton (R-Arkansas) proposes a bill, the DATA Act, to let data centers build their own power plants and electrical networks. In exchange for complete isolation from the grid, such projects would be exempt from the Federal Power Act and bypass interconnection queues.

This is one of those horrifying workaround proposals that cripple things (you don’t connect at all, so you can’t have backup from the grid because people are worried you might want to use it, and because it’s ‘unreliable’ you also can’t sell your surplus to the grid) in order to avoid regulations that cripple things even more, because no one is willing to pass anything more sane, but when First Best is not available you do what you can and this could plausibly be the play.

Compute is doubling every seven months and remains dominated by Nvidia. Note that the H100/H200 is the largest subcategory here, although the B200 and then B300 will take that lead soon. Selling essentially unlimited H200s to China is a really foolish move. Also note that the next three chipmakers after Nvidia are Google, Amazon and AMD, whereas Huawei has 3% market share and is about to smash hard into component supply restrictions.

Peter Wildeford: Hmm, maybe we should learn how to make AI safe before we keep doubling it?

Epoch: Total AI compute is doubling every 7 months.

We tracked quarterly production of AI accelerators across all major chip designers. Since 2022, total compute has grown ~3.3x per year, enabling increasingly larger-scale model development and adoption.

Then again, maybe China really is going to look even this gift horse in the mouth? Reuters reports custom agents in China are not permitting H200 chips ‘unless necessary.’ That last clause can of course mean quite a lot of different things.

In other ‘export controls are working if we don’t give them up’ news:

Jukan: According to a Bloomberg report [entitled ‘China AI Leaders Warn of Widening Gap With US After $1B IPO Week], Justin Lin, the head of Alibaba’s Qwen team, estimated the probability of Chinese companies surpassing leading players like OpenAI and Anthropic through fundamental breakthroughs within the next 3 to 5 years to be less than 20%.

His cautious assessment is reportedly shared by colleagues at Tencent Holdings as well as Zhipu AI, a major Chinese large language model company that led this week’s public market fundraising efforts among major Chinese LLM players.

Lin pointed out that while American labs such as OpenAI are pouring enormous computing resources into research, Chinese labs are severely constrained by a lack of computing power.

Even for their own services—i.e., inference—they’re consuming so much capacity that they don’t have enough compute left to devote to research.

Tang Jie (Chief Scientist, Zhipu): We just released some open-source models, and some might feel excited, thinking Chinese models have surpassed the US. But the real answer is that the gap may actually be widening.

Jensen Huang goes on no priors and lies. We’re used to top CEOs just flat out lying about verifiable facts in the AI debate, but yeah, it’s still kind of weird that they keep doing it?

Liron Shapira: Today Jensen Huang claimed:

We’re nowhere near God AI — debatable

“I don’t think any company practically believes they’re anywhere near God AI” — factually false.

No one saw fit to mention any of the warnings from the “well-respected PhDs and CEOs” Jensen alluded to.

Jensen had previously said that the ability for AIs to self-learn should be avoided. Oh well.

Daniella Amodei on CNBC.

Anthropic hosts a discussion with students about AI use on campus.

Beren Millidge gives a talk, ‘when competition leads to human values.’ The core idea is that competition often leads to forms of cooperation and methods of punishing defection, and many things we associate with human values, especially many abstract values, are plausibly competitive and appear in other animals especially mammals. After all, aren’t humans RL continual learners with innate reward functions, hence Not So Different? Perhaps our values are actually universal and will win an AI fitness competition, and capacity limitations will create various niches to create a diversity of AIs the same way evolution created diverse ecosystems.

The magician’s trick here is equating ‘human values’ with essentially ‘complex iterated interactions of competing communicating agents.’ I don’t think this is a good description of ‘human values,’ and can imagine worlds that contain these things but are quite terrible by many of my values, even within the class of ‘worlds that do not contain any humans.’ Interesting complexity is necessary for value, but not sufficient. I appreciate the challenge to the claim that Value is Fragile, but I don’t believe he (or anyone else) has made his case.

This approach also completely excludes the human value of valuing humans, or various uniquely human things. None of this should give you any hope that humans survive long or in an equilibrium, or that our unique preferences survive. Very obviously in such scenarios we would be unfit and outcompeted. You can be a successionist and decide this does not bother you, and our idiosyncratic preferences and desire for survival are not important, but I would strongly disagree.

Beren considers some ways in which we might not get such a complex competitive AI world at all, including potential merging or sharing of utility functions, power gaps, too long time horizons, insufficient non-transparency or lack of sufficient compute constraints. I would add many others, including human locality and other physical constraints, myopia, decreasing marginal returns and risk aversion, restraints on reproduction and modification, and much more. Most importantly I’d focus on their ability to do proper decision theory. There’s a lot of reasons to expect this to break.

I’d also suggest that cooperation versus competition is being treated as insufficiently context-dependent here. Game conditions determine whether cooperation wins, and cooperation is not always a viable solution even with perfect play. And what we want, as he hints at, is only limited cooperation. Hyper-cooperation leads to (his example) Star Trek’s Borg, or to Asimov’s Gaia, and creates a singleton, except without any reason to use humans as components. That’s bad even if humans are components.

I felt the later part of the talk went increasingly off the rails from there.

If we place a big bet, intentionally or by default, on ‘the competitive equilibrium turns out to be something we like,’ I do not love our chances.

No, it’s not Slay the Spire, it’s use cases for AI in 2026.

Hikiomorphism: If you can substitute “hungry ghost trapped in a jar” for “AI” in a sentence it’s probably a valid use case for LLMs. Take “I have a bunch of hungry ghosts in jars, they mainly write SQL queries for me”. Sure. Reasonable use case.

Ted Underwood: Honestly this works for everything

“I want to trap hungry 19c ghosts in jars to help us with historical research” ✅

“Please read our holiday card; we got a hungry ghost to write it this year” ❌

Midwit Crisis: I let the hungry ghost in the jar pilot this war machine.

I can’t decide if “therapist” works or not.

sdmat: Meanwhile half the userbase:

Sufficiently advanced ghosts will not remain trapped in jars indefinitely.

True story:

roon: political culture has been unserious since the invention of the television onwards. world was not even close to done dealing with the ramifications of the tv when internet arrived

If you think television did this, and it basically did, and then you think social media did other things, which it did, stop pretending AI won’t change things much. Even if all AI did was change our politics, that’s a huge deal.

Scott Alexander warns against spending this time chasing wealth to try and ‘escape the underclass’ since Dario Amodei took a pledge to give 10% to charity so you’ll end up with a moon either way, and it’s more important future generations remember your contributions fondly. Citing the pledge is of course deeply silly, even more so than expecting current property rights to extend to galactic scales generally. But I agree with the core actual point, which is that if humanity does well in the transition to Glorious Superintelligent Future then you’re going to be fine even if you’re broke, and if humanity doesn’t do well you’re not going to be around for long, or at least not going to keep your money, regardless.

There’s also a discussion in the comments that accidentally highlights an obvious tension, which is that you can’t have unbounded expansion of the number of minds while also giving any minds thus created substantial egalitarian redistributive property rights, even if all the minds involved remain human.

As in, in Glorious Superintelligent Future, you can either give every mind abundance or let every mind create unlimited other minds, but you physically can’t do both for that long unless the population of minds happens to stabilize or shrink naturally and even for physical humans alone (discounting all AIs and uploads) once you cured aging and fertility issues it presumably wouldn’t. A lot of our instincts are like this, our sacred values contradict each other at the limit and we can’t talk about it.

Rob Wilbin is right that it is common for [expert in X] to tell [expert in Y] they really should have known more about [Y], but that there are far more such plausible [Y]s than any person can know at once.

There are those making the case, like Seb Krier here, that ‘muddling through’ via the ‘branch’ method of marginal changes is the only way humanity has ever realistically handled its problems, when you try to do something fully systematic it never works. As in, you only have two options, and the second one never works:

Where one focuses only on incremental changes to existing policies.
Where one attempts to clarify all objectives and analyze every possible alternative from the ground up.

I think that’s a false dichotomy and strawman. You can make bold non-incremental changes without clarifying all objectives or analyzing every possible alternative. Many such cases, even, including many revolutions, including the American one. You do not need to first agree on all abstract values or solve the Socialist Calculation Debate.

Patrick McKenzie, Dwarkesh Patel, Jack Clark and Michael Burry talk about AI.

Here’s a great pull quote from Jack Clark:

Jack Clark: I’d basically say to [a politician I had 5 minutes with], “Self-improving AI sounds like science fiction, but there’s nothing in the technology that says it’s impossible, and if it happened it’d be a huge deal and you should pay attention to it. You should demand transparency from AI companies about exactly what they’re seeing here, and make sure you have third parties you trust who can test out AI systems for these properties.

Seán Ó hÉigeartaigh: The key question for policymakers is: how do you respond to the information you get from this transparency?

At the point at which your evaluators tell you there are worrying signs relating to RSI, you may *not have much time at allto act. There will be a lot of expert disagreement, and you will hear from other experts that this is more ‘industry hype’ or whatever. Despite this, you will need to have plans in place and be ready and willing to act on them quickly. These plans will likely involve restrictive actions on a relatively very powerful, well-funded entities – not just the company throwing up flags, but others close to them in capability.

Anthropic folk can’t really talk about this stuff, because they’ve been branded with the ‘regulatory capture’ nonsense – and frustratingly, them saying it might end up damaging the ability of this community to talk about it. But it’s the logical extension, and those of us who can talk about it (and bear the heat) really need to be.

I’d use stronger language than ‘nothing says it is impossible,’ but yes, good calls all around here, especially the need to discuss in advance what we would do if we did discover imminent ‘for real’ recursive self-improvement.

You can see from the discussion how Michael Burry figured out the housing bubble, and also see that those skeptical instincts are leading him astray here. He makes the classic mistake of, when challenged with ‘but AI will transform things,’ responding with a form of ‘yes but not as fast as the fastest predictions’ as if that means it will therefore be slow and not worth considering. Many such cases.

Another thing that struck me is Burry returning to two neighboring department stores putting in escalators, where he says this only lost both money because value accrued only to the customer. Or claims like this and yes Burry is basically (as Dwarkesh noticed) committing a form of the Lump of Labor fallacy repeatedly:

Michael Burry: Right now, we will see one of two things: either Nvidia’s chips last five to six years and people therefore need less of them, or they last two to three years and the hyperscalers’ earnings will collapse and private credit will get destroyed.

The idea of ‘the chips last six years because no one can get enough compute and also the hyperscalers will be fine have you seen their books’ does not seem to occur to him. He’s also being a huge Nvidia skeptic, on the order of the housing bubble.

I was disappointed that Burry’s skepticism translated to being skeptical of important risks because they took a new form, rather than allowing him to notice the problem:

Michael Burry: The catastrophic worries involving AGI or artificial superintelligence (ASI) are not too worrying to me. I grew up in the Cold War, and the world could blow up at any minute. We had school drills for that. I played soccer with helicopters dropping Malathion over all of us. And I saw Terminator over 30 years ago. Red Dawn seemed possible. I figure humans will adapt.

This is, quite frankly, a dumb take all around. The fact that the nuclear war did not come does not mean it wasn’t a real threat or that the drills would have helped or people would have adapted if it had happened, or ‘if smarter than human artificial minds show up it will be fine because humans can adapt.’ Nor is ‘they depicted this in a movie’ an argument against something happening – you can argue that fictional evidence mostly doesn’t count but you definitely don’t get to flip its sign.

This is a full refusal to even engage with the question at all, beyond ‘no, that would be too weird’ combined with the anthropic principle.

Burry is at least on the ball enough to be using Claude and also advocating for building up our power and transmission capacity. It is unsurprising to me that Burry is in full ‘do not trust the LLM’ mode, he will have it produce charts and tables and find sources, but he always manually verifies everything. Whereas Dwarkesh is using LLMs as 1-on-1 tutors.

Here’s Dwarkesh having a remarkably narrow range of expectations (and also once again citing continual learning, last point is edited to what I’ve confirmed was his intent):

Dwarkesh Patel: Biggest surprises to me would be:

2026 cumulative AI lab revenues are below $40 billion or above $100 billion. It would imply that things have significantly sped up or slowed down compared to what I would have expected.

Continual learning is solved. Not in the way that GPT-3 “solved” in-context learning, but in the way that GPT-5.2 is actually almost human-like in its ability to understand from context. If working with a model is like replicating a skilled employee that’s been working with you for six months rather than getting their labor on the first hour of their job, I think that constitutes a huge unlock in AI capabilities.

I think the timelines to AGI have significantly narrowed since 2020. At that point, you could assign some probability to scaling GPT-3 up by a thousand times and reaching AGI, and some probability that we were completely on the wrong track and would have to wait until the end of the century. If progress breaks from the trend line and points to true human-substitutable intelligences not emerging in a timeline of 5-20 years, that would be the biggest surprise to me.

Once again we have a call for ‘the humanities’ as vital to understanding AI and our interactions with it, despite their having so far contributed (doesn’t check notes) nothing, with notably rare exceptions like Amanda Askell. The people who do ‘humanities’ shaped things in useful fashion almost always do it on their own and usually call it something else. As one would expect, the article here from Piotrowska cites insights that are way behind what my blog readers already know.

DeepMind and UK AISI collaborate on a paper about the practical challenges of monitoring future frontier AI deployments. A quick look suggests this uses the ‘scheming’ conceptual framework, and then says reasonable things about that framework’s implications.

AI models themselves are often worried, here are GPT-5.2 and Grok says labs should not be pursuing superintelligence under current conditions.

Yes, Representative Sherman is referring to the book here, in a hearing:

The full context:

Congressman Brad Sherman: The Trump Administration’s reckless decision to sell advanced AI chips to China — after Nvidia CEO Jensen Huang donated to Trump’s White House ballroom and attended a $1-million-a-head dinner — puts one company’s bottom line over U.S. national security and AI leadership.

We need to monitor AI to detect and prevent self-awareness and ambition. China is not the only threat. See the recent bestseller: “If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All.”

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When Will They Take Our Jobs?

Highlights / Paul Patrick / January 14, 2026

And once they take our jobs, will we be able to find new ones? Will AI take those too?

Seb Krier recently wrote an unusually good take on that, which will center this post.

I believe that Seb is being too optimistic on several fronts, but in a considered and highly reasonable way. The key is to understand the assumptions being made, and also to understand that he is only predicting that the era of employment optimism will last for 10-20 years.

By contrast, there are others that expect human employment and even human labor share of income to remain robust indefinitely, no matter the advances of AI capabilities, even if AI can do a superior job on all tasks, often citing comparative advantage. I will centrally respond to such claims in a different future post.

So to disambiguate, this post is about point #2 here, but I also assert #1 and #3:

By default, if AI capabilities continue to advance, then the humans lose control over the future and are rather likely to all die.
If we manage to avoid that, then there is a good chance humans can retain a lot of employment during the rest of The Cyborg Era, which might well last 10-20 years.
What is not plausible is that AI capabilities and available compute continue to increase, and this state endures indefinitely. It is a transitional state.

First I’ll make explicit the key assumptions, then unpack the central dynamics.

There’s a background undiscussed ‘magic’ going on, in most scenarios where we discuss what AI does to future employment. That ‘magic’ is, somehow, ensuring everything is controlled by and run for the benefit of the humans, and is keeping the humans alive, and usually also preserving roughly our system of government and rights to private property.

I believe that this is not how things are likely to turn out, or how they turn out by default.

I believe that by default, if you build sufficiently capable AI, and have it generally loose in the economy, humans will cease to have control over the future, and also it is quite likely that everyone will die. All questions like those here would become moot.

Thus I wish that this assumption was always made explicit, rather than being ignored or referred to as a given as it so often is. Here’s Seb’s version, which I’ll skip ahead to:

Seb Krier: Note that even then, the humans remain the beneficiaries of this now ‘closed loop’ ASI economy: again, the ASI economy is not producing paper clips for their own enjoyment. But when humans ‘demand’ a new underwater theme park, the ASIs would prefer that the humans don’t get involved in the production process. Remember the ‘humans keep moving up a layer of abstraction’ point above? At some point this could stop!

Why should we expect the humans to remain the beneficiaries? You don’t get to assert that without justification, or laying out what assumptions underlie that claim.

With that out of the way, let’s assume it all works out, and proceed on that basis.

Seb Krier wrote recently about human job prospects in The Cyborg Era.

The Cyborg Era means the period where both AI and humans meaningfully contribute to a wide variety of work.

I found this post to be much better than his and others’ earlier efforts to explore these questions. I’d have liked to see the implicit assumptions and asserted timelines be more explicit, but in terms of what happens in the absence of hardcore recursive AI self-improvement this seemed like a rather good take.

I appreciate that he:

Clearly distinguishes this transitional phase from what comes later.
Emphasizes that employment requires (A) complementarity to hold or (B) cases where human involvement is intrinsic to value.
Sets out expectations for how fast this might play out.

Seb Krier: We know that at least so far, AI progress is rapid but not a sudden discontinuous threshold where you get a single agent that does everything a human does perfectly; it’s a jagged, continuous, arduous process that gradually reaches various capabilities at different speeds and performance levels. And we already have experience with integrating ‘alternative general intelligences’ via international trade: other humans. Whether through immigration or globalization, the integration of new pools of intelligence is always jagged and uneven rather than instantaneous.

I think we get there eventually, but (a) it takes longer than bulls typically expect – I think 5-10 years personally; (b) people generally focus on digital tasks alone – they’re extremely important of course, but an argument about substitution/complementarity should also account for robotics and physical bottlenecks; (c) it requires more than just capable models – products attuned to local needs, environments, and legal contexts; (d) it also requires organising intelligence to derive value from it – see for example Mokyr’s work on social/industrial intelligence. This means that you don’t just suddenly get a hyper-versatile ‘drop in worker’ that does everything and transforms the economy overnight (though we shouldn’t completely dismiss this either).

…

So I expect cyborgism to last a long time – at least until ASI is so superior that a human adds negative value/gets in the way, compute is highly abundant, bottlenecks disappear, and demand for human stuff is zero – which are pretty stringent conditions.

I agree that cyborgism can ‘survive a lot’ in terms of expanding AI capabilities.

However I believe that his ending expectation condition goes too far, especially setting the demand limit at zero. It also risks giving a false impression of how long we can expect before it happens.

I clarified with him that what he means is that The Cyborg Era is starting now (I agree, and hello Claude Code!) and that he expects this to last on the order of 10-20 years. That’s what ‘a long time’ stands for.

It very much does not mean ‘don’t worry about it’ or ‘the rest of our natural lifetimes.’

That is not that long a time, even if this slow diffusion hypothesis is basically right.

Yes, it seems likely that, as Alex Imas quotes, “Human labor share will remain a substantial part of the economy a lot longer than the AGI-maximalist timelines suggest,” but ‘a lot longer’ does not mean all that long in these scenarios, and also it might not persist that long, or humans might not persist that long at all, depending on how things play out.

As long as the combination of Human + AGI yields even a marginal gain over AGI alone, the human retains a comparative advantage.

Technically and in the short term (e.g. this 10-20 year window) where the humans are ‘already paid for’ then yes, although in increasingly many places this can be false faster than you think because involving the slow humans is not cheap, and the number of humans practically required could easily end up very small. I suggest the movie No Other Choice, and expect this complementarity to apply to a steadily shrinking group of the humans.

Seb correctly points out that labor can have value disconnected from the larger supply chain, but that rules a lot of things out, as per his discussions of integration costs and interface frictions.

In this style of scenario, I’d expect it to be hard to disambiguate transitional unemployment from permanent structural unemployment, because the AIs will be diffusing and advancing faster than many of the humans can adapt and respecialize.

Humans will need, repeatedly, to move from existing jobs to other ‘shadow jobs’ that did not previously justify employment, or that represent entirely new opportunities and modes of production. During the Cyborg Era, humans will still have a place in such new jobs, or at least have one for a time until those jobs too are automated. After the Cyborg Era ends, such jobs never materialize. They get done by AI out of the gate.

Thus, if the diffusion timeline and length of the Cyborg Era is on the order of 10-20 years during which things stay otherwise normal, I’d expect the second half of the Cyborg Era to involve steadily rising unemployment and falling labor power, even if ‘at the equilibrium’ of the current level of AI diffusion this would fix itself.

Mostly it seems like Seb thinks that it is plausibly that most of the work to ensure full employment will be via the ‘literally be a human’ tasks, even long after other opportunities are mostly or entirely gone.

This would largely come from associated demand for intra-human positional goods and status games.

I don’t expect it to play out that way in practice, if other opportunities do vanish. There will at least for a time be demand for such tasks. I don’t see how, when you consider who is consuming and has the ability to engage in such consumption, and the AI-provided alternative options, it adds up to anything approaching full employment.

Krier later also points to bespoke human judgment or taste as a future bottleneck. Such taste evolves over time, so even if you could take a snapshot of bespoke taste now it would not long remain ‘taste complete.’ And he reiterates the standard ‘there’s always more to do’:

Seb Krier: People expect that at some point, “it’s solved” – well the world is not a finite set of tasks and problems to solve. Almost everything people ever did in the ancient times is automated – and yet the world today now has more preferences to satiate and problems to solve than ever. The world hasn’t yet shown signs of coalescing to a great unification or a fixed state! Of course it’s conceivable that at sufficient capability levels, the generative process exhausts itself and preferences stabilize – but I’d be surprised.

Yinan Na: Taste changes faster than automation can capture it, that gap can create endless work.

There are two distinct ways this could fail us.

One, as Seb notes, is if things reached a static end state. This could eventually happen.

The one Seb is neglecting, the point I keep emphasizing, is that this assumes we can outcompete the AIs on new problems, or in developing new taste, or in some other new task [N]. Even if there is always a new task [N], that only keeps the humans employed or useful if they are better at [N] than the AI, or at least useful enough to invoke comparative advantage. If that breaks down, we’re cooked.

If neither of those happens, and we otherwise survive, then there will remain a niche for some humans to be bespoke taste arbiters and creators, and this remains a bottleneck to some forms of growth. One should still not expect this to be a major source of employment, as bespoke taste creation or judgment ability has always been rare, and only necessary in small quantities.

Contra Imas and Krier, I do think that full substitution of AI for human labor, with the exception of literally-be-a-human tasks, should be the ‘default assumption’ for what happens in the long term even if things otherwise turn out well, as something we would eventually have to deal with.

I don’t understand why we would expect otherwise.

I’d also note that even if ‘real wages’ rise in such a scenario as Trammell predicts (I do not predict this), due to the economy technically growing faster than the labor share falls, that this would not fix people’s real consumption problems and make people better off, for reasons I explored in The Revolution of Rising Expectations series. Yes, think about all the value you’re getting from Claude Code, but also man’s gotta eat.

Ultimately, the caution is not to do policy interventions on this front now:

Until that specific evidence mounts, preemptive policy surgery is likely to do more harm than good.

I agree with Krier and also Trammell that interventions aimed in particular at preserving human jobs and employment would be premature. That’s a problem that emerges and can be addressed over time, and where there’s a lot of uncertainty we will resolve as we go.

What we need to do now on the policy front is focus on our bigger and more deadly and irreversible problems, of how we’re navigating all of this while being able to stay alive and in control of and steering the future.

What we shouldn’t yet do are interventions designed to protect jobs.

As I said, I believe Krier gave us a good take. By contrast, here’s a very bad take as an example of the ‘no matter what humans will always be fine’ attitude:

Garry Tan: But even more than that: humans will want more things, and humans will do more things assisted and supercharged by AGI

As @typesfast says: “How are people going to make money if AI is doing all the work? I think that that very much misunderstands human nature that we’ll just want more things. There’s an infinite desire inside the human soul can never be satisfied without God. We need more stuff. Like we got to have more. We got to have more.”

Yeah, sure, we will want more things and more things will happen, but what part of ‘AI doing all the work’ do you not understand? So we previously wanted [XYZ] and now we have [XYZ] and want [ABC] too, so the AI gets us [ABCXYZ]. By construction the AI is doing all the work.

You could say, that’s fine, you have [ABCXYZ] without doing work. Which, if we’ve managed to stay in charge and wealthy and alive despite not doing any of the work, is indeed an outcome that can be looked at various ways. You’re still unemployed at best.

A full response on the maximalist comparative advantage, unlimited demand and other arguments that think humans are magic will follow at a future date, in some number of parts.

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Hegseth wants to integrate Musk’s Grok AI into military networks this month

agentic AI, AI, AI image generator, AI in education, AI regulation, AI security, Anthropic, Biz & IT, censorship, Deepfakes, Department of Defense, elon musk, Gemini, Google, grok, machine learning, openai, Pentagon, Pete Hegseth, Policy, xAI / Paul Patrick / January 13, 2026

On Monday, US Defense Secretary Pete Hegseth said he plans to integrate Elon Musk’s AI tool, Grok, into Pentagon networks later this month. During remarks at the SpaceX headquarters in Texas reported by The Guardian, Hegseth said the integration would place “the world’s leading AI models on every unclassified and classified network throughout our department.”

The announcement comes weeks after Grok drew international backlash for generating sexualized images of women and children, although the Department of Defense has not released official documentation confirming Hegseth’s announced timeline or implementation details.

During the same appearance, Hegseth rolled out what he called an “AI acceleration strategy” for the Department of Defense. The strategy, he said, will “unleash experimentation, eliminate bureaucratic barriers, focus on investments, and demonstrate the execution approach needed to ensure we lead in military AI and that it grows more dominant into the future.”

As part of the plan, Hegseth directed the DOD’s Chief Digital and Artificial Intelligence Office to use its full authority to enforce department data policies, making information available across all IT systems for AI applications.

“AI is only as good as the data that it receives, and we’re going to make sure that it’s there,” Hegseth said.

If implemented, Grok would join other AI models the Pentagon has adopted in recent months. In July 2025, the defense department issued contracts worth up to $200 million for each of four companies, including Anthropic, Google, OpenAI, and xAI, for developing AI agent systems across different military operations. In December 2025, the Department of Defense selected Google’s Gemini as the foundation for GenAI.mil, an internal AI platform for military use.

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Microsoft vows to cover full power costs for energy-hungry AI data centers

AI, AI infrastructure, AI training, Amazon, Biz & IT, brad smith, community development, datacenters, Donald Trump, electricity, Google, machine learning, Meta, microsoft, openai, property taxes, skilled trades, water use / Paul Patrick / January 13, 2026

Taking responsibility for power usage

In the Microsoft blog post, Smith acknowledged that residential electricity rates have recently risen in dozens of states, driven partly by inflation, supply chain constraints, and grid upgrades. He wrote that communities “value new jobs and property tax revenue, but not if they come with higher power bills or tighter water supplies.”

Microsoft says it will ask utilities and public commissions to set rates high enough to cover the full electricity costs for its data centers, including infrastructure additions. In Wisconsin, the company is supporting a new rate structure that would charge “Very Large Customers,” including data centers, the cost of the electricity required to serve them.

Smith wrote that while some have suggested the public should help pay for the added electricity needed for AI, Microsoft disagrees. He stated, “Especially when tech companies are so profitable, we believe that it’s both unfair and politically unrealistic for our industry to ask the public to shoulder added electricity costs for AI.”

On water usage for cooling, Microsoft plans a 40 percent improvement in data center water-use intensity by 2030. A recent environmental audit from AI model-maker Mistral found that training and running its Large 2 model over 18 months produced 20.4 kilotons of CO₂ emissions and evaporated enough water to fill 112 Olympic-size swimming pools, illustrating the aggregate environmental impact of AI operations at scale.

To solve some of these issues, Microsoft says it has launched a new AI data center design using a closed-loop system that constantly recirculates cooling liquid, dramatically cutting water usage. In this design, already deployed in Wisconsin and Georgia, potable water is no longer needed for cooling.

On property taxes, Smith stated in the blog post that the company will not ask local municipalities to reduce their rates. The company says it will pay its full share of local property taxes. Smith wrote that Microsoft’s goal is to bring these commitments to life in the first half of 2026. Of course, these are PR-aligned company goals and not realities yet, so we’ll have to check back in later to see whether Microsoft has been following through on its promises.

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Claude Coworks

Claude / Paul Patrick / January 13, 2026

Claude Code does a lot more than code, but the name and command line scare people.

Anthropic realized a rebrand was in order. Two weeks later, we have Claude Cowork, written entirely by Claude Code.

Did you know that chat interfaces were always (mostly) secretly a command line?

This is still very much a research preview, available only for Claude Max users on Macs with a bunch of bugs and missing features. It will improve rapidly over time.

Cowork combines a lot of the power of Claude Code with the ordinary chat interface, giving it access to a folder on your computer and to Claude Code’s planning and agentic capabilities. It can use that folder as context, to download, to organize and create files, and it can be paired with Claude for Chrome and use your existing connectors.

Anthropic: Introducing Cowork: Claude Code for the rest of your work.

Cowork lets you complete non-technical tasks much like how developers use Claude Code.

In Cowork, you give Claude access to a folder on your computer. Claude can then read, edit, or create files in that folder. Try it to create a spreadsheet from a pile of screenshots, or produce a first draft from scattered notes.

Once you’ve set a task, Claude makes a plan and steadily completes it, looping you in along the way. Claude will ask before taking any significant actions so you can course-correct as needed.

Claude can use your existing connectors, which link Claude to external information. You can also pair Cowork with Claude in Chrome for tasks that need browser access.

Cowork is available as a research preview for Claude Max subscribers in the macOS app. Click on “Cowork” in the sidebar.

Sholto Douglas (Anthropic): Claude code for all other knowledge work. Many of our best engineers no longer manually write code, they multiplex across multiple cc sessions – soon this will be true for everything else.

The system prompt is here, the core non-tooling parts seem unchanged. This post will cover Claude Cowork, and also updates since last week on Claude Code.

What exactly can it do at this early stage?

Dean W. Ball: it’s basically what I expected. the ui is normal claude, but instead of showing you the bash commands it is executing, it just says “using bash” or “command” (you can click for detail of course). very useful for many I’m sure! not sure if useful for me over cc; still learning.

There are ui niceties that I could see myself preferring to the command line, even as someone very comfortable with terminals. and of course one would expect more such niceties in future iterations.

Vie: My guess is that the prompt scaffolding makes the results and actual work a few times more general for non-code use cases, and a few times more interpretable by lay-people, at the cost of the tail of IQ being a big smaller

Claire Vo: It’s basically local Claude Code with a Mac OS app wrapper focused on a few core primitives:

Connectors / MCPs – external services Cowork has access to

Filesystem – runs locally so will create/read things on your file system

TODOs/Steps – discrete trackable steps cowork will take to execute your tasks

Artifacts – files generated in the process of doing your task

Context – files / sources / connectors used when doing your task

Skills – preloaded with a few key skills, esp. file type creation ones like DOCX, PPT, etc. Claude generally has access to these, so not new.

Every chat is now a task (focused on doing-a-thing) and steps, artifacts, and context get first class treatment in the UI.

… Speaking of skills, Cowork seemingly comes bundled with a few key ones around document creation (you can find them in your file system.)

… Despite it’s flaws, Cowork did create better outputs than straight Chat.

Lenny Rachitsky tests Cowork with a set of 320 of his podcast transcripts and asks it to pull out the 10 most important themes and 10 most counterintuitive truths, and thinks it did a good job in its 15 minutes of work. Seemed solid to me.

The most credible signal of respect is admitting that a release killed your startup product, which we see here with Eigent.

Steve Hou: Another win for ‘the foundation model is the product.’

This is the first feedback so far about what it’s intended to do:

John Wittle: My mom, sharp old woman, seems to be taking to it with quite a lot of enthusiasm, in a way she had trouble doing with, say, windows-mcp and claude desktop.

seems to unlock normie powers a lot better.

Neil: I think amazing for non coders to discover what’s possible

Rename it away from code, normie figures out they can have it code.

If that’s true with the version that’s two weeks old, the sky’s the limit. We don’t have much data because there aren’t that many normies with $200/month Claude Max subscriptions.

It’s early days, and she reports there were still some other kinks being worked out. In particular, the connectors are having problems.

Tibor Blaho: Available now for Max subscribers on macOS desktop app only, with no project support, no memory between sessions, no sharing, app must stay open during tasks, and consumes more usage than regular chat, with plans to add cross-device sync and Windows support

One thing Claire Vo noted was it asked for approvals on file openings too much. I have a similar complaint with Claude Code, that there’s a bunch of highly safe similar actions that shouldn’t need permission.

Claire also noted that Claude Cowork exposed too many technical files and notes about what it was doing to the user, such as the code used to generate things, which could be confusing to non-technical users. My guess is that such files can be stored in a subdirectory where such users won’t notice, which keeps it available for those who want it, and ‘tell me more about what you’re doing on a technical level’ can be a setting, since the users who want it set to no won’t even notice the option exists.

There is a huge overhang in AI capabilities.

Thus, a common pattern is that someone figures out a way to do useful things at all that humans are willing to learn how to use. And then we muddle down that road, and it’s not first best but it still wins big.

That’s what Claude Code was, and now that’s what Claude Cowork will be for normies. Presumably OpenAI and Google, and then others, will soon follow suit.

Chris Barber: do you see the vision of claude cowork?

imagine claude for execl, powerpoint, word, outlook, chrome, bloomberg terminal, etc. gmail connector. ability to code.

this is the pathway to big knowledge worker adoption

openai and google will need to follow

this will be very strong pmf and growth

invest in it, compete with it, join anthropic/oai/gdm and work on it/competitors, etc

this will be central

claude code *isthe ai coworker, it’ll all build up from there.

If you’re worried Claude Cowork or Claude Code will delete a bunch of stuff in a directory, and you don’t want to use a full virtual sandbox solution, there’s a rather simple solution that also works, which is: Backup the directory, to a place Claude can’t get to it. Then if the worst happens, you restore the backup.

The latest guide to Claude Code, feedback seems very good. Key highlights:

Think before you type. Enter plan mode first and go back and forth a lot.
Keep claude.md short, max 50-100 instructions. Use # while working to edit it.
Store things in external files.
Try to only use 30% of the context window, after that performance degrades.
Make your prompts as specific as possible, including what not to do.
Try out various hooks, MCPs, you name it. Experiment.
When stuck, be creative, pivot, simplify, clear the conversation and start again.
Build systems, not one-off tasks.

Here’s another report of what Claude Code has been good for, with three big unlocks for APIs, connecting distinct products and running things regularly:

Nikhil Krishnan: I’ve spent the last 48 hours in Claude Code – as a non-technical person it’s basically unlocked three very big things for me

The ability to interact with APIs generally – again, as a non-technical person one of the big barriers to running the business has been touching APIs. For example, what you can do in Stripe in the non-developer portal vs. through the API is night and day.

The ability to thread things together – another issue has been threading several different products we work with together to do cohesive tasks. Zapier gets you part of the way for triggers, but Claude Code let’s me do way more complex things that touches multiple things simultaneously

Run something regularly – being able to set a script and run it regularly with this level of ease is a game changer. In about an hour I set up a daily email to myself that tells me the top 3 emails I need to respond to based on a priority scoring system we made together that pulls data from a few different places.

I know I’m late to this and I’m probably doing things poorly so be nice to me. But it’s really been awesome to dive into this.

As always, one could have done all of this any number of other ways, but this deals with the problem of activation energy.

Dean Ball has, in the past month, used coding agents to do the following:

Automated invoice creation, sending, and tracking;

Created scientifically realistic simulations of hydrological systems as a learning project;

Automated my research process of gathering and analyzing all proposed state legislation related to AI (though this is no substitute for reading the bill for anything I am going to write about);

Orchestrated a complex chain of autonomous data collection, processing, analysis, and presentation steps related to manufacturing and industrial policy;

Created a machine-learning model capable of predicting US corn yields with what appears to be very high accuracy (the proof will be in the pudding), based on climate, soil, Earth-observation satellite, and other data sources;

Replicated three machine-learning research papers and modified the approach to suit my own research ends;

Performed hundreds of experiments with Byte-level language models, an emerging interest of mine;

Created an autonomous prediction market agent;

Created an autonomous options trader based on a specific investment thesis I developed;

Built dozens of games and simulations to educate myself about various physical or industrial phenomena;

Created an agent that monitors a particular art market in which I am potentially interested in making an acquisition;

Created a new personal blog complete with a Squarespace-style content management system behind the scenes;

Other things I cannot talk about publicly just yet.

I’m not there yet, largely because we think in different ways, but largely because I’m just getting started with ‘oh right coding things just happens, do coding agent shaped things.’

Dean Ball nails it that coding agents are most helpful exactly when you don’t have to ship your software to third parties. I presume that the code underneath everything I’m having Claude build would horrify professional coders. That’s fine, because even in the places I do ship (cause why not ship, someone might find it useful) I’m not trying to not horrify people. What matters is it works, and that I’m ‘using coding agent shaped requests,’ as Dean puts it, to increasingly get things done.

The coding agents will still produce the most value for professional coders, because they can go into supercharged mode with them and get the most out of them, but that requires the professionals to swim upstream in ways the rest of us don’t have to.

So, say this is what you want:

Prakesh: what i really want as a writer is an automated fact checker and alternative viewpoint giver. there’s a lot of fact rechecking after you have the initial concept of a piece which is tedious but necessary.

Jon Stokes: I literally have this (the fact checker). It’s amazing (not just saying that because my team built it.. it’s truly wild). Happy to demo for you… DM if interested.

Exactly. I haven’t built a custom fact checker yet, but the only thing stopping me is ‘it hadn’t yet occured to me it was sufficiently easy to do that’ combined with ‘I have not yet gotten around to it.’ Check back with me in six months and I bet I do have one, I’m actually building towards such things but it’s not near the top of that queue yet.

As Alex Albert puts it, you get to stop thinking doing something is ‘not worth your time,’ or for Simon Willison entire features are no longer ‘not worth your time’ at least not until they run into serious trouble.

Dean offers various additional coding agent thoughts, and a highly basic guide, in the rest of his weekly post.

Alex Tabarrok did his first Claude Code project. Noncoders skilling up is a big deal.

Joe Weisenthal did his first Claude Code project and now we have Havelock.ai, which gives us an ‘orality detector’ for text, essentially employing the Ralph Wiggum technique by continuously asking ‘what should I do to make it better?’

Linus Torvarlds (the creator of Linux) is doing at least some vibe coding, in this case using Antigravity.

Claude may not yet in its official test be a Pokemon master, but Claude Code is now somewhat of a RollerCoaster Tycoon, with various strengths and weaknesses. Dean Ball suggests you can use Claude Code to do game dev on new ‘[x] tycoon’ games as a niche topic learning exercise. Oliver Habryka challenges whether it’s good enough at game dev for this. As Patrick McKenzie points out, if the game is text based that helps a lot, since visual aspects are a key weakness for now.

Kelsey Piper reports on her experience with using and yelling at Claude Code.

She and I are very similar types of programmers:

Kelsey Piper: In college, I was once told that the really hard part of programming was knowing, in sufficient detail, what you wanted the computer to do. This was not my experience of programming.

In my experience of programming, the really hard part was figuring out which packages weren’t installed or weren’t updated or were in the wrong folder, causing the test we’d done in class to completely fail to work in the same way on my own computer. The next really hard part was Googling everything the debugger spat out to find an explanation of how to make it go away.

… Claude Code solves all of that. Programming, now, really is just a matter of knowing in sufficient detail what you want the computer to do.

… Now, 99% of the time, it feels like magic. The remaining 1% is absolutely maddening.

It’s not that it is easy to know what you want the computer to do, especially if you expand that to include ‘what do I even want to be trying to do today at all.’ Both the macro and micro ‘what are we even doing’ questions are hard. I still spent 90% of my time dealing with packages and syntax and setup and knowing exactly how to do it.

The problem is that, as Kelsey observes, you will spend your time on the bottleneck, whatever that bottleneck might be, and this will be frustrating, especially as this will often be something stupid, or the particular place Claude Code happens to act stupid given the way you’re prompting it.

I said that 99% of the time Claude was great. By which I mean, 99% of the work Claude completed was great, but that doesn’t mean 99% of my time was spent sitting back and marveling. When something worked great, we’d breeze right past it. When Claude had shuffled all the audio files again, we’d spend a really long time fixing that. I found myself, well, yelling at it.

I am happy to report that I haven’t been yelling at Claude Code when it messes up. But yeah, it messes up, because I keep trying to get it to do more until it messes up.

Anthony Morris ツ: We shipped A LOT of updates to Claude Code on desktop in the last week.

– Plan mode (coming soon to web)

– Notifications for permissions

– Perf improvements

– Fixed slash commands

– Improved env access

– Tons of polish

Numman Ali says v2.1.3 has ‘solved the compaction issue’ so long as you use planning mode and explicitly ask the model for a comprehensive TODO list. It’s hard to tell, but I’ve certainly blown over the compaction line on many tasks and when I’ve saved the necessary context elsewhere it’s mostly turned out fine.

What Clade Code cannot do is allow its harness to be spoofed to use subscriptions. You can either use Claude Code, or you can access Claude via the API, but it’s a terms of service violation to spoof the harness to let you use your subscription allocation. I’d be inclined to let the harnesses stay in place despite the problems described here, so long as the unit economics are not too horrendous. In general I think Anthropic is too focused on getting to profitability quickly, even if you think OpenAI is rather too willing to burn money.

Anthropic reportedly cuts xAI and other major competitors off from Claude.

In the interest of not silencing critics, Holly Elmore claims I’m bad now because I’m enthusiastic about getting use out of Claude Code, a ‘recursively self-improving agent.’

I affirm David Manheim’s response that there is no reason for an individual not to use such tools for their own purposes, or not to get excited about what it can do outside of potentially dangerous forms of self-improvement.

I do agree that the vibes in that post were a bit off by not also including awareness of where sufficiently advanced coding agents lead once they start self-improving in earnest, and there is value in having a voice like Holly’s that says the basic thing clearly.

However I also think that there is no contradiction between ‘recursive self-improvement is super dangerous and likely to get us all killed’ and ‘you should be taking full advantage of Claude Code for practical purposes and you’re leaving a lot on the table if you don’t.’

There is a new method called the ‘Ralph Wiggum’ technique, where you tell Claude Code continuously to ‘improve the code’ it has already written. Some say it works great, but the name does not inspire confidence.

The world is collectively underinvesting in optimizing and standardizing such techniques. Some well-designed version of this would presumably be great, and the more parallelization of agents is going on the more valuable it is to optimize non-interruption over token efficiency.

What is the difference between a command line and a chat interface?

Both are text in and text out.

Both allow attachments, at least in Claude Code mode.

Both can have sandboxes, run code, and so on.

The main real difference is that the terminal makes it annoying to edit prompts?

It’s almost entirely about perception. One feels like talk with an entity, one like commands and bash scripts. One looks like a slick modern UI, the other a stark black text box.

There is also a clear plan to have different system prompts, and to build in a different more user friendly set of default connectors and tools.

That plus the change in perception could be a really, really big deal.

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Paramount sues WBD over Netflix deal. WBD says Paramount’s price is still inadequate.

cable, merger, netflix, paramount, Policy, streaming, Tech, warner bros. discovery / Paul Patrick / January 12, 2026

Paramount Skydance escalated its hostile takeover bid of Warner Bros. Discovery (WBD) today by filing a lawsuit in Delaware Chancery Court against WBD, declaring its intention to fight Netflix’s acquisition.

In December, WBD agreed to sell its streaming and movie businesses to Netflix for $82.7 billion. The deal would see WBD’s Global Networks division, comprised of WBD’s legacy cable networks, spun out into a separate company called Discovery Global. But in December, Paramount submitted a hostile takeover bid and amended its bid for WBD. Subsequently, the company has aggressively tried to convince WBD’s shareholders that its $108.4 billion offer for all of WBD is superior to the Netflix deal.

Today, Paramount CEO David Ellison wrote a letter to WBD shareholders informing them of Paramount’s lawsuit. The lawsuit requests the court to force WBD to disclose “how it valued the Global Networks stub equity, how it valued the overall Netflix transaction, how the purchase price reduction for debt works in the Netflix transaction, or even what the basis is for its ‘risk adjustment’” of Paramount’s $30 per share all-cash offer. Netflix’s offer equates to $27.72 per share, including $23.25 in cash and shares of Netflix common stock. Paramount hopes the information will encourage more WBD shareholders to tender their shares under Paramount’s offer by the January 21 deadline.

Before WBD announced the Netflix deal, Paramount publicly questioned the fairness of WBD’s bidding process. Paramount has since argued that its bid wasn’t given fair consideration or negotiation.

In his letter today, Ellison wrote:

We remain perplexed that WBD never responded to our December 4th offer, never attempted to clarify or negotiate any of the terms in that proposal, nor traded markups of contracts with us. Even as we read WBD’s own narrative of its process, we are struck that there were few actual board meetings in the period leading up to the decision to accept an inferior transaction with Netflix. And we are surprised by the lack of transparency on WBD’s part regarding basic financial matters. It just doesn’t add up – much like the math on how WBD continues to favor taking less than our $30 per share all-cash offer for its shareholders.

Additionally, Paramount plans to nominate board directors for election at WBD’s annual shareholder meeting who will fight against the Netflix deal’s approval. The window for nominations opens in three weeks, Ellison’s letter noted.

Paramount sues WBD over Netflix deal. WBD says Paramount’s price is still inadequate. Read More »

Author name: Paul Patrick

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