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40% of middle and high school students spend about half the school day on devices

A recent NYT survey of US teachers reveals how much screen time American students get in class.

Tom Jones

In March 2020, when the pandemic had kicked off in earnest and schools shut around the world, educational institutions rushed to source e-learning devices to ensure kids could keep learning from home. Now, as we hurtle time-warpingly close to six years on from that point, students are back in class as normal, except they now arrive equipped with the devices that helped them through the remote learning era.

Plugged in

Per an October survey from The New York Times published by The Upshot last week, 8 in 10 American teachers say that students at their schools now have devices assigned to them, up from around a third who said the same in 2019. While that might not be the most surprising news to anyone who has kids or knows even a little bit about modern classroom dynamics, the amount of time some students are spending on those screens each day may come as a little more of a shock.

Student screen time chart
Sherwood News

As you might expect, tablets, laptops, and other school-issued tech are relied on less heavily in elementary schools, with a healthy majority of students spending less than one hour a day on devices in the classroom, according to their teachers. Older kids, however, spend more time with devices in school. Per the most recent Pew Research Center figures, the average school day is around six hours in the US, meaning that the 40% of middle and high school students who spend more than three hours on devices see at least half of their time in school consumed by tech.

Teachers are already worried about screens in schools — a different part of the survey revealed that 70% of respondents said devices are distracting students at least “a little” from schoolwork. Still, despite how you may personally feel, all of this becomes pretty tricky when you remember that many of the so-called “good jobs” that parents and teachers may hope children get are, when stripped back, basically just eight hours spent staring at a screen all day.

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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.

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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.

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.

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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 Streets 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: Teslas 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.

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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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