[AI DAILY NEWS RUNDOWN TEASER] The ARC-AGI-3 Reality Check, Google’s TurboQuant Compression, and Palantir’s Pentagon Lock-in (March 26th 2026)

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🚀 Welcome to AI Unraveled. Today, the AI industry gets a massive reality check. A new benchmark proves the top LLMs still cannot reason, forcing the enterprise to pivot toward Small Language Models (SLMs) and inference compression. Meanwhile, Meta quietly buys up every autonomous agent startup on the market, and the Pentagon turns Palantir into permanent military infrastructure.

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In Today’s Briefing:

• The ARC-AGI-3 Humiliation: Why frontier models from OpenAI and Google are scoring below 1% on a reasoning test that humans ace.

• Google TurboQuant: A new algorithm that compresses AI model memory by over 6x with zero accuracy loss, threatening the AI hardware market.

• The Rise of SLMs: Neurometric launches an SLM Marketplace, proving that 75% of enterprise tasks are better suited for cheap, sub-20B parameter models.

• Meta’s Talent Monopoly: Mark Zuckerberg acqui-hires Dreamer, adding to a $2B shopping spree for agentic AI talent alongside a $135B capex budget.

  

• The Pentagon’s Palantir Lock-in: The U.S. military is spending $13.4 billion on AI in 2026, elevating Palantir’s Maven to core infrastructure.

• Reddit’s Bot Crackdown: CEO Steve Huffman deploys World ID and passkeys to separate humans from AI agents.

• The EU AI Act Omnibus: Europe delays enterprise AI compliance to 2028, but implements an immediate, strict ban on AI “nudifier” deepfake apps.

• Mistral Voxtral TTS: A new open-weight text-to-speech model that runs entirely on-device and supports 9 languages.

Strategic Signal: The Cap on LLM Reasoning vs. The Optimization of Inference.

Keywords: ARC-AGI-3 Benchmark, François Chollet, Google TurboQuant, Neurometric SLM, Meta Dreamer Acquisition, Palantir Maven Pentagon, Reddit Bot Crackdown World ID, EU AI Act Nudifier Ban, Mistral Voxtral TTS, Small Language Models, DjamgaMind, AIRIA, AI Unraveled.

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⚗️ PRODUCTION NOTE: We Practice What We Preach.

AI Unraveled is produced using a hybrid “Human-in-the-Loop” workflow.

ARC’s new AGI test stumps every frontier AI

Image source: ARC Prize Foundation

François Chollet’s ARC Prize Foundation just released ARC-AGI-3, the newest version of its interactive reasoning benchmark, where humans can solve 100% of tasks on the first try but AI models struggle, with top systems not even scoring 1%.

The details:

• Labs spent millions training models on earlier versions of the test, pushing ARC-AGI-2 scores from 3% to around 50% in under a year.

• Agents face game-like scenarios with zero instructions, and must discover rules, form goals, and plan strategies entirely from scratch.

• Google’s Gemini Pro scored the highest among frontier models at just 0.37%, followed by GPT 5.4 High (0.26%), Opus 4.6 (0.25%), and Grok-4.20 (0%).

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• A $1M prize backs the challenge, and cofounder Mike Knoop says frontier labs are paying far more attention to V3 than they did to earlier versions.

Why it matters: It’s always jarring to see the top models get reset below 1% on a new ARC-AGI release, but if the older tests are any indicator, even more surprising will be how quickly frontier labs climb the ladder. Whether that reflects genuine reasoning or just more expensive brute-forcing is exactly what Chollet built V3 to find out.

Reddit’s AI bot crackdown skips the ID check

Image source: Reddit

Reddit CEO Steve Huffman outlined a plan to separate humans from bots across the site, including labeling automated accounts, flagging suspicious users for verification, and letting sub-communities self-police without mass ID checks.

The details:

• Accounts running automation in approved ways on the social platform will carry an [App] label, with suspicious behavior leading to human verification.

• To confirm proof of humanity, Reddit will offer passkeys or Sam Altman’s World ID scanner, with government IDs as a last resort, only where laws require it.

• AI-written content isn’t being banned, with Huffman calling it ‘annoying’ but saying communities can set their own rules on AI-generated posts.

• Rival platform Digg recently folded after being overrun with bots, and Cloudflare data shows automated traffic on pace to surpass humans by 2027.

Why it matters: The Dead Internet Theory was already here before the AI agent acceleration we’ve seen over the past six months. Now, it’s a reality every social media site is dealing with. While this feels a bit like a band-aid, it is a small step towards every platform needing a serious human-first solution if it wants to remain usable to them.

OpenAI pauses erotic chatbot plans indefinitely LINK

• OpenAI has paused its plans to launch an erotic “adult mode” for ChatGPT indefinitely, confirming to the Financial Times that it is shifting focus toward its core products instead.

  
  
  

• The company wants more time to research the potentially harmful effects of sexually explicit chats and the emotional attachments they may create, while investors also weren’t excited about the project.

• This is the second major product OpenAI pulled back this week, after discontinuing its Sora AI video-generation app to redirect compute power to other higher-priority projects going forward.

Apple may build smaller AI models from Gemini LINK

• Apple has gained full access to Google’s Gemini model and plans to distill it into smaller models that can run directly on Apple devices without an internet connection.

• The distillation process works by feeding Gemini’s high-quality answers and reasoning information into smaller, cheaper models that learn its internal computations while requiring less computing power.

• Apple is building a smarter, chatbot version of Siri for iOS 27 using Gemini, but has hit issues because Gemini was tuned for chatbot and coding tasks that don’t always match Apple’s needs.

Quantum computers could break encryption by 2029, warns Google LINK

• Google published a formal plan to move all of its infrastructure to post-quantum cryptography by 2029, warning that quantum computers capable of breaking current encryption may arrive sooner than expected.

• The company highlighted “harvest now, decrypt later” attacks as an already active threat, where bad actors steal encrypted data today planning to unlock it once quantum machines become powerful enough.

• Over 6.8 million Bitcoin sitting in vulnerable addresses could eventually be at risk, but Bitcoin developers have started work on quantum-resistant upgrades through BIP 360, a new address format proposal.

Google shrinks AI memory with zero accuracy loss

Image source: Google

Google Research introduced TurboQuant, an algorithm that compresses AI model memory over 6x without any retraining — while delivering up to 8x speed gains on Nvidia H100 chips and losing almost zero accuracy.

The details:

• AI models keep a running log of each conversation, and as chats get longer, that storage balloons, which slows responses and drives up costs.

• TurboQuant shrinks that storage by over 6x with zero accuracy loss, scoring perfectly on tests that bury a key detail in a large amount of text.

• On Nvidia’s top server chips, it also sped up response processing up to 8x compared to standard methods, without adding any extra cost to run.

• The paper, set to be presented at ICLR 2026 in April, also topped rival methods in vector search — the tech search engines use to match similar results quickly.

Why it matters: Despite being first published in April 2025, top AI memory companies felt the heat of the official release, with stocks dropping 3-5%. One compression paper won’t crater memory demand overnight, but the selloff shows Wall Street is pricing in a world where smarter software cuts into the premium AI memory commands.

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Meta acqui-hires its 4th AI startup in 4 months

Mark Zuckerberg isn’t just building models; he’s assembling an entire talent army for agents. Meta just acqui-hired the team behind Dreamer (an agentic AI platform), adding them to Meta Superintelligence Labs.

• The move follows Meta’s $2B acquisition of autonomous web agent Manus in December, the acqui-hire of AI social network Moltbook in early 2026, and bringing on Scale AI’s Alexandr Wang as Chief AI Officer.

• Meta is concurrently developing an internal AI model (reported codename: “Avocado”) and has projected ~$135 billion in 2026 capital expenditures.

• The pattern shows Meta aggressively cornering the market on agentic talent to build a decentralized “decision-support layer” across its platforms.

Pentagon formalizes Palantir’s Maven AI with $13B investment

The Pentagon is formalizing Palantir’s Maven AI as a core military system with multi-year funding, pushing the platform’s investment umbrella to $13 billion (up from $480 million in 2024). The US Defense Department is spending a staggering $13.4 billion on AI this year alone.

• The jump from “defense contractor” to “core military infrastructure” concerns some civilian tech watchdogs, who note that infrastructure doesn’t get shut down when it hallucinates; it just gets patched.

• Military AI at this funding level is moving faster than civilian accountability frameworks, prioritizing verifiable targeting over explainable decisions.

European Parliament bans AI “nudifier” apps LINK The European Parliament adopted its position on a simplification (”omnibus”) proposal amending the Artificial Intelligence Act. While the EU is delaying the application of certain high-risk AI system rules to 2027/2028 to give companies breathing room, it took immediate action on deepfakes.

• MEPs introduced a strict new ban on “nudifier” systems that use AI to create or manipulate sexually explicit images resembling an identifiable real person without consent.

• The legislation also gives AI providers until November 2026 to comply with rules on mandatory watermarking for AI-created audio, image, video, or text content.

Google upgrades Lyria 3 Pro for full song generation

Google has massively upgraded its music AI model, rolling out Lyria 3 Pro across Gemini, Vertex AI, and Google Vids.

• The model can now generate full 3-minute songs with structured intros, verses, choruses, and bridges from a single prompt.

• The audio quality rivals studio-produced tracks, further threatening the economics of the commercial stock music industry.

Clarivate reveals the “AI50” Global Leaders LINK A new data-driven benchmark by Clarivate reveals that the vast majority of high-impact AI inventions are concentrated in just four regions. Of the top 50 global organizations leading AI invention, 15 are in China, 14 in the US, 6 in South Korea, and 6 in Japan.

Mistral gives hands-free productivity a boost

A month ago, Mistral conquered speech-to-text with its Voxtral Transcribe 2 model family. Now, the AI lab is flipping the script with its first text-to-speech model.

On Thursday, Mistral launched its Voxtral TTS, an open-weight text-to-speech model that is three times smaller than the industry standard, more natural-sounding, and multilingual. The company claims that it’s the best open-source text-to-speech model released to-date.

The model was built to be as natural-sounding as possible by understanding the context of what people say and modeling how people naturally speak, including pauses, rhythm, intonation, emotion and more. Some other highlights include:

• Support for nine languages: English, French, German, Spanish, Dutch, Portuguese, Italian, Hindi, and Arabic. It can accept input in one language and output in another.

• Speed: Adapts to voices with references of just five seconds and offers low-latency, taking a voice sample of 10 seconds and 500 characters, and producing audio within 90 milliseconds

• On-device: 3 billion parameter model that fits in a smartwatch, a phone, or a laptop

• Human evaluations: In comparative human evaluation of Voxtral TTS and ElevenLabs v2.5 Flash, Mistral’s model achieved superior naturalness

While there are many other text-to-speech models on the market, Pierre Stock, VP of science and the first employee hired at Mistral AI, said the model’s overall efficiency sets it apart.

“The model is optimal for both efficiency — including the cost, the speed, the ease of deployment – and state-of-the-art, conversational capability in which the model feels both super natural and conversational,” Stock told The Deep View.

Some use cases, Mistral notes, include customer support, sales and marketing, real-time translation and personal voice agents. The model is available to test out in Mistral Studio playground, and a model with several reference voices is available as open weights on Hugging Face.

Can SLMs solve AI’s biggest roadblock in 2026?

When I talk to people putting AI to work in organizations right now, I typically hear two things: either inference costs are out of control or they are paying too much for tokens. Spoiler: those two things mean the same thing. The cost of running AI is too high, and AI agents are making it a lot worse.

Here’s the good news (or bad news, depending on your POV): If you’re building AI agents, most of the tokens your agent consumes are going to a frontier model that’s wildly overqualified for the job, and you’re overpaying for it.

We’re talking about tasks like:

• Classifying a support ticket

• Extracting a date from an email

• Rewriting a subject line

These are not tasks that require models with hundreds of billions of parameters. A small, fine-tuned model can handle them in milliseconds at a fraction of the cost.

In an exclusive interview with The Deep View, Rob May, CEO of Neurometric, shared how the company is betting big on this mismatch.

On Wednesday, Neurometric launched its SLM Marketplace, a catalog of 115 task-specific small language models (SLMs). Each of these models is under 20 billion parameters and is fine-tuned to do one thing well. You can download any model for free, or let Neurometric host it with up to 100 million tokens per month at no charge. After that, it costs $2 per month per model.

“100 million tokens on one of these models costs us 40 cents,” May told me. “So why would we charge for it?”

May says they are seeing a 25/75 split: roughly 25% of tasks in a typical enterprise AI workflow genuinely need frontier-level reasoning. The other 75% are structured, repetitive, and narrowly focused, such as classification, extraction, formatting, routing, scoring, and summarization. SLMs can handle those tasks.

The models in the SLM Marketplace span 14 categories, from accounting and finance to developer tools. Neurometric expects to scale to 5,000 models by year’s end. For tasks not yet covered, an Auto-SLM Creator can generate custom models from a plain-language description.

One insight that stood out from my conversation with May was that the biggest challenge with small models is keeping them on track. Larger models handle this naturally, but SLMs need what May described as “micro harnesses,” or checkpoint prompts that remind the model what it’s doing and what comes next. It’s part of an emerging discipline called harness engineering.

The next generation of electricity generation

By Bill Gates | March 26, 2026

I’m in Texas this week for the Breakthrough Energy Ventures Investors Summit, and the energy here (no pun intended) is incredible. It also matches what’s happening in the industry writ large, especially when it comes to electricity.

By 2050, the world will need to generate nearly three times as much electricity as we use today. And we need to generate it cleanly. That’s a tremendous challenge—but it’s also an exciting moment to be paying attention. Earlier this month, TerraPower, the nuclear company I started in 2008, received federal approval to begin construction at its Wyoming plant. Wind and solar are generating more electricity than fossil fuels in the EU for the first time ever. The way we power the world is changing fast.

What’s driving that change? New approaches to geothermal, fusion energy, and geologic hydrogen—some that are closer to commercialization than most people realize, and some that might still be decades away.

On my website, I’ve written about three companies in this space that I think are worth watching. You can read about them, and why I’m so optimistic about the future of clean electricity, on Gates Notes now.

What Else Happened in AI on March 26th 2026?

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OpenAI is raising another $10B to push its record funding round past $120B, with Microsoft, a16z, and T. Rowe Price joining the round.

Google upgraded its music AI model to generate full 3-minute songs with intros, verses, and choruses, with Lyria 3 Pro rolling out in Gemini, Vertex AI, and Google Vids.

Bret Taylor’s Sierra introduced Ghostwriter, an AI agent that builds other AI agents — letting companies create customer service bots across voice, chat, and 30+ languages.

The U.S. Department of Labor launched “Make America AI-Ready,” a free 7-day AI literacy course delivered entirely over text message to promote AI upskilling.