Meta CEO Mark Zuckerberg Testifies In Social Media Addiction Trial

(Wally Skalij/Getty Images)

vertically challenge

First Snapchat, now TikTok: Instagram has mastered the art of borrowing features

The latest user data suggests its pivot to video with Reels over five years ago has paid off.

3/2/26 9:59AM

Facebook was not really close to being the first name in the social media game, with SixDegrees, MySpace, Friendster, and a long list of now defunct sites exploring the idea of creating platforms to connect people online before Mark Zuckerberg’s company. However, it was Facebook, despite arriving somewhat late to the party, that won out in the end, iterating on the idea and focusing on college campuses like the one where its 19-year-old creator found himself at the time.

The Meta CEO hasn’t really been shy to borrow, or simply buy, good ideas ever since.

When Instagram was blowing up, Zuck and co. saw a potential threat to Facebook, eventually buying the app that had just 13 employees for $1 billion. When Snapchat’s “stories” concept proved wildly popular, it wasn’t long before Instagram had the same feature... with the same name. More recently, after Elon Musk relaunched Twitter as X, Meta was quick to push out Threads, its text-based alternative.

Back when a new challenge emerged from the then Chinese-owned TikTok, Instagram replied with Reels, its own vertical feed, in August 2020. Now, more than five years later, it’s fair to say that Meta seems to have pulled it off again.

The reel deal

While it might be true that if you see a funny TikTok video, you can expect to see the same content recycled on Reels a few weeks later, for Insta users (and Meta’s mighty advertising machine), it doesn’t seem to matter. The once photo-focused app’s daily user count has overtaken TikTok’s in the US once more, according to data from Similarweb, a digital market intelligence company.

Instagram and TikTok users chart — Sherwood News

Now, the compelling, algorithmic content feeds that dominate the time and attention of people the world over are so powerful that not a week goes by without policymakers, scientists, and millions of everyday doomscrollers wondering about the negative effects they might have on their lives.

Indeed, the very concept of the “infinite scroll,” made popular by TikTok, is so addictive that the European Union is looking to kill the feature entirely.

Jon Keegan3h

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

Rani Molla

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.

Jon Keegan9h

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

Rani Molla10h

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.

Jon Keegan10h