New York City Just Hit Pause on AI in Classrooms

New York City Public Schools is drawing a hard line: no generative AI for students from 2-K through eighth grade during the 2026-2027 school year. That’s over half a million kids walking into classrooms next week without access to AI-powered tools.

The district says it will remove software with student-facing AI features and block AI companion chatbots. High schoolers? They’re exempt. This isn’t a blanket ban—it’s a targeted move to decide when kids should actually start using the technology.

Mayor Zohran Mamdani put it bluntly: “The tech industry wants us to believe that AI-powered early education is not only inevitable, but necessary. We do not see it that way.”

Why NYC Is Worried About AI for Younger Students

The fear isn’t just that a kid will ask ChatGPT to write an essay. City officials want younger students to build core skills—critical thinking, creativity, communication—without leaning on AI as a crutch. They’re also pushing for stronger human connections in classrooms, not another screen.

Schools Chancellor Kamar Samuels said the city refuses to assume that innovation automatically equals more technology in front of students. It’s a deliberate slowdown, and it follows last year’s bell-to-bell cellphone ban that already limits device use during school hours.

What Happens When Kids Reach High School?

AI doesn’t vanish once students hit ninth grade. Instead, the district plans to roll out AI literacy classes twice a year. The goal? Teach teenagers how to think critically about the technology before they become dependent on it.

That’s a different approach from just saying no. It’s about timing—letting younger minds develop without AI, then giving older students the tools to question it.

The National Battle Over AI in Education

NYC’s decision sits at the center of a much bigger fight. The White House has pushed educators to embrace AI responsibly. Some teachers already use it to craft lesson plans, give feedback, or break down tough subjects. But not everyone’s on board.

The Department of Health and Human Services recently gathered childhood experts to talk about excessive screen time. Officials have also called for tougher safeguards around social media and AI. The message? Kids are spending too much time in front of screens, and AI might make it worse.

A Bold Experiment With Zero AI

Here’s what makes NYC’s move so interesting: instead of asking how much AI younger students should use, the largest school district in the country is starting with none. For one academic year, they’re testing whether classrooms are better off with AI kept outside the door.

That’s a radical stance, and it’s not without critics. Some educators argue AI can personalize learning or help struggling students catch up. But NYC is betting that a year without AI will reveal what kids actually need—not what tech companies think they need.

What This Means for Parents and Teachers

If you’re a parent in NYC, expect changes. AI-based apps may disappear from your child’s school day. Teachers will need to plan lessons without generative AI tools. And students in grades 2-K through 8 will rely more on traditional methods—paper, pencils, and human interaction.

For teachers elsewhere, this could be a signal. NYC is the biggest district to take this stance, and its findings could shape policies nationwide. The next year will be watched closely by educators, policymakers, and tech giants alike.

What Happens Next?

The district will spend the year studying how generative AI affects students before deciding what comes next. That research could lead to a permanent ban, a partial rollout, or something entirely different.

For now, NYC is making a statement: childhood shouldn’t be an AI beta test. Whether that’s the right call or a step backward, we’ll know more in 2027. Until then, the debate over AI in schools just got a lot more interesting.

If you’re curious about how AI is shaping other areas, check out our take on AI in education trends or classroom technology policies.

Meta just fired a serious shot in the AI arms race

The company quietly unleashed Muse Spark 1.3, its most advanced AI model to date, and it’s aiming straight at the top dogs. Developers can already access and pay for the update, which Meta says represents a massive leap forward in performance.

It won’t stay confined to the developer sandbox for long. Over the coming weeks, the model will roll out across Instagram, Facebook, and the Meta AI assistant — putting it in front of billions of everyday users.

What makes Muse Spark 1.3 actually different?

Meta’s Chief AI Officer, Alexandr Wang, didn’t mince words. He called this “the biggest jump so far on model performance,” pointing to serious gains in two areas: coding and agentic tasks — the kind where the AI acts on your behalf rather than just answering questions.

But here’s the catch. Wang told Bloomberg that Muse Spark 1.3 is competitive with Anthropic’s Claude Fable 5.1 and even beats OpenAI’s GPT-5.6 Sol at coding. That’s a bold claim, especially with OpenAI’s upcoming Astra model lurking in the wings.

Take those comparisons with a grain of salt, though. Benchmarks can be gamed, and a model that crushes one test might stumble on a totally different task. Real-world performance is what actually matters.

Efficiency gains under the hood

Wang broke down a few upgrades that set Muse Spark 1.3 apart:

• 25% fewer tokens needed to complete the same job — meaning lower costs and faster responses
• Multi-workflow handling — it can juggle several tasks at once instead of forcing separate sessions
• Better context retention across long, complicated instructions
• Self-awareness of limits — the model now pauses to ask for clarification before taking any irreversible action

That last point is quietly important. AI that knows when it doesn’t know is a big step toward trustworthiness, especially for agentic use cases.

Pricing stays flat, adoption explodes

Here’s something developers will appreciate: Meta isn’t raising prices. Muse Spark 1.3 costs the same as its predecessor, Muse Spark 1.2. That’s a smart move when rivals are hiking rates.

Wang told Bloomberg that adoption on Meta’s developer platform has been strong — some users are burning through trillions of tokens every week. Those numbers suggest real usage, not just hype.

What about open-source fans? Meta hasn’t decided whether it will release the model’s weights — the blueprint that lets outside developers build on top of it. The older Muse Spark 1.2 weights are still headed for release, but the new model’s future remains unclear.

The bigger picture: Meta’s spending spree continues

Meta is still pouring billions into AI infrastructure, and Muse Spark 1.3 is the clearest signal yet that the company believes it’s closing the gap with OpenAI and Anthropic. Whether that’s true or just corporate bravado will play out in the benchmarks and real-world deployments over the coming months.

For now, the model is available to developers, and the app rollout is imminent. If you’re building on Meta AI tools, this update is worth a serious look. And if you’re just a curious user, you’ll likely meet Muse Spark 1.3 in your Instagram feed sooner than you think.

One thing’s certain: the AI race just got a lot more interesting.

Meta pulls AI usage out of the performance review equation

If you’ve been tracking how Meta evaluates its workforce, you know the company spent the last year pushing employees to embrace artificial intelligence with unusual zeal. Engineers were told their performance reviews would factor in “AI-driven impact.” Workers could earn labels like “AI Native” or “AI First” based on how deeply they integrated chatbots into their daily routines. It sounded forward-thinking—until it turned into a contest over who could burn through the most AI tokens.

Now Meta is walking it back. According to a new report from WIRED, the company has updated its performance-review guidance so employees are no longer judged on how much they use AI tools. The new language focuses on something far more traditional: the actual impact of an employee’s work. Outcomes, the guidance now says, “can be supported by AI or other means.”

That last part matters. Meta isn’t banning AI or telling people to stop experimenting. It’s simply saying that using the tools isn’t the achievement itself.

How we got here: the strange rise of ‘tokenmaxxing’

The shift didn’t happen in a vacuum. Last year, Meta’s performance-review criteria explicitly mentioned AI adoption, which created an odd incentive structure. Some employees began prompting AI tools more frequently just to inflate their internal usage numbers. They watched their token counts—the chunks of information AI models process—climb like a video game score.

Things got competitive enough that one employee built an internal leaderboard ranking colleagues by AI usage. The rankings came with titles. The top tier? “Token Legend.” Yes, that was a real thing inside Meta. The leaderboard eventually disappeared after details leaked publicly earlier this year.

You can see why Meta would want to hit reset. When workers are competing to become “Token Legends,” the actual work product starts to feel secondary.

What employees were told this week

Engineers received the updated guidance earlier this week. The message was direct: AI adoption dashboards and token counts will not be used to measure your impact. No more gaming the system by generating endless queries. No more treating your AI usage dashboard like a fitness tracker.

Meta spokesperson Tracy Clayton told WIRED that the company has always evaluated workers based on their contributions. Clayton also said labels like “AI Native” were never part of the formal performance evaluation process—even if employees felt otherwise.

Meta’s AI ambitions aren’t cooling off

Don’t read this as Meta losing interest in artificial intelligence. Far from it. The company is pushing forward on multiple fronts, and employees are getting access to increasingly capable tools.

Take Hatch, an experimental AI agent that Meta employees have been testing on their corporate devices for several weeks. Unlike a conventional chatbot that mostly answers questions, Hatch can take actions on a computer. It browses the web, interacts with other applications, and completes tasks on a user’s behalf. It’s the kind of agentic AI that tech companies have been promising for years, and it could eventually see a public release.

So Meta’s approach is more nuanced than it might appear. Employees are still encouraged to experiment with powerful AI systems. They’re just not rewarded for raw usage anymore.

Why this matters beyond Meta

Meta’s reversal is a small but telling signal for the broader tech industry. Over the past two years, companies have rushed to integrate AI into their workflows, often measuring adoption through metrics like token consumption or tool usage. The problem? Those metrics can become targets in themselves—a phenomenon some researchers have called “metric fixation.”

When usage becomes the goal, employees optimize for the wrong thing. They generate more tokens, not better outcomes. They prompt AI tools because they’re being watched, not because the tools genuinely help. Meta’s internal leaderboard was an extreme example, but the underlying dynamic is hardly unique.

For anyone working in HR or management, the lesson is straightforward: measure outcomes, not activity. If an employee delivers a brilliant project without touching a single AI tool, that should count for more than someone who burns through 10 million tokens and produces mediocre work.

What’s next for Meta’s workforce

The updated review language gives employees more breathing room. They can experiment with AI where it makes sense, and skip it where it doesn’t. That’s a healthier approach, and it aligns with how Meta is positioning its AI tools internally.

The company is still investing heavily in AI infrastructure, and tools like Hatch suggest that Meta sees a future where AI agents handle routine tasks across its platforms. But the performance review change signals that Meta wants its employees to be thoughtful about AI, not obsessive.

It’s a fine line to walk. Meta clearly wants its workforce to be AI-literate and ready for what’s next. But it also wants to avoid the kind of performative usage that turns a productivity tool into a status symbol.

Judging the work rather than the token counter? That’s probably the right call.

Key takeaways

• Meta removed AI usage and token counts from performance review criteria
• The change follows internal competition over AI tokens, including a “Token Legend” leaderboard
• Labels like “AI Native” were not part of formal evaluations, per Meta
• Meta is still encouraging AI adoption and testing an agentic AI tool called Hatch
• The shift reflects a broader industry lesson: measure outcomes, not activity