AI image of Sam Altman grilling Pikachu (@shlms/Sora)

Sora lasted less than one Quibi

OpenAI’s app joins the hallowed halls of video ideas that burned bright and fast.

3/25/26 12:23PM

Sora, we hardly knew ya. Yesterday, OpenAI announced that it will be shutting down its AI-generated text-to-video app, Sora.

Sora enjoyed a brief but intense moment of virality, juiced by its exclusive invitation-only early rollout. The app spent about three wild weeks as the No. 1 app in the iOS App Store during this invite-only period, but once everyone had access, interest started to drop.

With Sora shuttering, OpenAI’s app is added to the hallowed halls of short-lived video concepts like Quibi (Disney invested $25 million into that one), HQ Trivia, and Meta’s Lasso — a TikTok clone before Instagram Reels. Other examples, like the social audio app Clubhouse or the daily photo check-in app BeReal, have managed to survive in some form despite having lost much of their early hype.

When the app initially launched, users were quick to push it to generate some shocking videos. Dead celebrities like Robin Williams, Stephen Hawking, and Martin Luther King Jr. made frequent appearances in the early wave of videos.

Social media was flooded with user-generated videos featuring OpenAI CEO Sam Altman shoplifting on security camera footage, dressing up as a Nazi, and barbecuing Pikachu.

The number of recognizably copyrighted characters showing up in Sora videos was surprising, considering the bevy of lawsuits filed by content owners against AI companies like Midjourney. Hollywood was reportedly blindsided by OpenAI’s permissive rules around copyrighted characters, leading the company to roll out a plan allowing owners to opt their intellectual property out of appearing in Sora videos.

Sora’s launch led to an alternative to the “sue for an ungodly sum” model, when Disney and OpenAI announced a partnership in December of last year. The three-year agreement included a $1 billion Disney investment in OpenAI and would grant Sora users access to more than 200 animated Disney characters that they could prompt into doing, well, whatever.

As part of the deal, OpenAI reportedly wouldn’t pay a dime in cash for the licensing — an abnormal situation for the IP-obsessed entertainment juggernaut, but one that revealed a bit about where Disney saw the true value of the partnership (holding a piece of OpenAI).

Jon Keegan8h

FT: Meta considering “tens of billions” in new capital to fund AI

Just days after Google announced a monster $85 billion upsized equity raise, the extremely profitable Meta is seeking to sell “tens of billions of dollars” in stock, according to a new report from the Financial Times.

Meta is planning on spending between $125 billion and $145 billion on AI capital expenditure this year alone.

Shares dropped more than 5% on the news.

Meta weighs big equity raising after blockbuster Google deal

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Meta is supercharging its subscription businesses

The free-app giant never really cared about recurring revenue. Now, it’s launching paid plans for all sorts of stuff.

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FT: Anthropic staff helping the NSA use Mythos for offensive cyberattacks

Anthropic’s Mythos AI model was deemed too dangerous to release to the public, with the company citing its ability to orchestrate novel cyberattacks.

And that’s just what the National Security Agency is doing, with the help of Anthropic staff embedded at the agency, according to a report from the Financial Times.

Only a small number of companies and US allies have been given access to the advanced model, which means America’s adversaries have not had the chance to shore up their defenses against the AI model’s new offensive capabilities.

The arrangement is especially unusual as the Pentagon has deemed Anthropic’s AI a national security supply chain risk — effectively blacklisting it for defense work — in response to the company’s refusal to allow its technology to be used for any legal application, which could include autonomous killing or mass surveillance. Anthropic is currently suing the US government to fight the determination.

US National Security Agency using Anthropic’s Mythos for cyber attacks

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Longtime Tesla bear JPMorgan upgraded Tesla and raised its price target to $475 from $145

For more than a decade, JPMorgan was Wall Street’s most stubborn Tesla skeptic, anchored by auto analyst Ryan Brinkman’s strict focus on traditional car fundamentals and near-term delivery numbers.

But JPM recently handed coverage of the stock to a new analyst, Rajat Gupta, who is throwing that playbook out the window. In a note Friday, the firm upgraded Tesla to “neutral” from “underweight” and raised its price target 228% to $475 from $145. (The analyst consensus on FactSet is $403.) Instead of focusing on the company’s struggling vehicle business, the new analyst is orienting himself more toward Tesla’s idea of the future, now modeling Tesla’s physical AI and robotaxi fleets all the way out to the year 2040.

Here are the main reasons for the capitulation:

Looking past the car lot: Gupta argues that Tesla “is at the forefront of physical AI, entering uncharted TAMs” and therefore “deserves the benefit of the doubt to be valued on LT earnings potential” rather than near-term speed bumps.
Unmatched vertical integration: Tesla’s control over everything from battery cells to custom silicon gives it a massive moat. JPM notes this starting point advantage is “unmatched at an industrial level scale” and “still somewhat under-appreciated and misunderstood.”
The AWS flywheel effect: Deploying Optimus robots inside its own factories “should not only lower COGS for the base automotive business, but more importantly, help validate the product at an industrial scale.” Gupta called it “a classic flywheel effect, somewhat analogous to AWS and Kiva at AMZN.”

For Tesla bulls who have argued for years that this is an AI company and not a carmaker, JPM’s sudden $3.9 trillion valuation model is the ultimate validation.

Anthropic ponders self-improving AI

Anthropic says Claude already writes 80% of its code. A new post asks what happens when the models can improve themselves — and whether anyone could stop them.

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