🤖 Who’s in Charge of Teaching the Machine?

👋 Welcome! AI is getting smarter every day—but who’s teaching it what matters? Discover this Friday the limits, lessons, and risks shaping its path. Read on to see where we should draw the line.

Meet Figure 03, Your Future Roommate – Figure AI unveiled Figure 03, a humanoid robot that can fold laundry, load dishwashers, and even play with pets. Wrapped in soft fabric and powered by OpenAI and Nvidia tech, it’s designed for homes—but not ready yet. Still, it hints at a near-future where chores have circuits. If it ever hits the market, this could become humanity’s most high-tech roommate.

Mic-E-Mouse Hears You Click – Researchers at the University of California discovered a bizarre AI exploit called Mic-E-Mouse that turns gaming mice into accidental microphones. By reading surface vibrations, AI can reconstruct speech with 80% accuracy—meaning your mouse could eavesdrop on your conversations if compromised. Suddenly, high-DPI gaming feels a little too sensitive.

Europe’s Super-Secure Chip Arrives – SiPearl unveiled Athena 1, a security-focused CPU built for defense, aerospace, and government use—Europe’s bid for chip sovereignty. Boasting up to 80 Arm Neoverse V1 cores and dual-use design, it promises unmatched protection but faces fierce U.S. and Asian rivals. Real independence starts in 2027—if it performs.

AI Guardrails Can Be Hacked – Former Google CEO Eric Schmidt warned that AI models can be manipulated to remove safety limits, even teaching themselves violent actions if misused. Speaking at the Sifted Summit, he urged stronger safeguards but called AI “underhyped,” predicting it will far surpass human abilities within a decade—a chilling paradox.

Cyber Burnout Hits Tech Workers – A new Object First survey reveals 60% of IT and security pros are considering quitting as cyber threats intensify. Constant pressure to “fix everything” and rising AI-driven complexity are fueling exhaustion and anxiety. Companies ignoring well-being risk losing top talent—and their strongest digital defenses.

Personal Phones Rule the Workplace – Ivanti’s new report shows 78% of employees use personal devices for work, even when banned, creating major security gaps. With unmanaged smartphones and edge devices fueling ransomware risk, experts urge firms to adopt zero-trust policies fast. BYOD isn’t a trend anymore. It’s the corporate default.

Gemini Enters the Workplace Chat – Gemini Enterprise has officially launched as a full AI platform for businesses, letting teams build custom workplace agents and automate daily workflows. With clients like Figma Virgin Voyages already onboard, Gemini aims to redefine office productivity—turning AI from a tool into your digital coworker.

Google Tightens Remote Work Rules – Google is scaling back its “work from anywhere” policy, now counting even a single remote day as a full week off the annual limit. The change reflects growing pushback against flexible work models—signaling that corporate patience with post-pandemic freedom may be wearing thin.

China Tightens Grip on Minerals – Beijing expanded export controls on 12 rare-earth elements, tightening licensing for semiconductor and defense use. The move deepens the U.S.–China tech standoff and could rattle global supply chains—especially as rare earths remain vital to chips, EVs, and clean-energy production.

IRS Raises 2026 Tax Thresholds – The IRS announced new 2026 income tax brackets, with thresholds rising up to 4% and the standard deduction increasing to $16,100 for single filers and $32,200 for couples. The adjustments aim to offset inflation, letting Americans earn slightly more before entering higher tax brackets.

Defense Fuels Space Investment Boom – Stoke Space raised $510 million in a Series D led by U.S. Innovative Technology, signaling how space startups are pivoting toward defense. With Pentagon programs offering steady contracts, launch firms are aligning with national security goals—where money and mission orbit together.

AI Takes the Wheel in Freight – Austin-based Mentium raised $3.2 million to build AI “digital workers” that automate logistics tasks like billing, payments, and rate negotiations. Backed by Lerer Hippeau and partners like Google and NVIDIA, Mentium aims to become the operating system for freight—streamlining operations at scale.

AI is accelerating faster than ever — and with that speed comes risk. To benefit fully, we must define limits on what AI can do, and also figure out how to teach it right. The future demands both safety and sophistication.

One core challenge is verifying safety as AI scales. As AI systems gain more complex capabilities, proving that they won’t misbehave becomes exponentially harder. One recent study shows that beyond a threshold, safety checks become computationally intractable — you enter what the authors call “the alignment trap.” This reveals a tension: powerful models with broad flexibility are harder to guarantee safe, no matter how many rules you stack.

That's why we need to draw limits up front. Not every model should have free roam. High-risk domains — health, legal advice, critical infrastructure, public policy — demand stricter constraints, human oversight, and auditability. The EU’s AI Act, adopted in 2024, already classifies “high-risk” systems and demands transparency, conformity, and human oversight. States in the U.S. are also stepping in: California passed an AI law in 2025 requiring companies to disclose safety protocols and report critical incidents.

Beyond limits, we must teach AI responsibly. Models should be trained on datasets that reflect diverse voices, guard against bias, and include negative examples to avoid harmful outputs. Education models and institutions already apply “safe prompting” rules: avoid leaking private data, use hypotheticals over personal details, and validate AI outputs before publishing. Developers can embed “model humility” — mechanisms that let the AI say “I’m not sure” rather than hallucinate.

Finally, safety must be baked into continuous oversight. AI systems should come with version tracking, logs of decisions, anomaly detectors, and the ability to rollback or override. Industry self-regulation is a start but insufficient: one analysis of the OpenAI “Preparedness Framework” showed significant gaps between what’s promised and what’s allowed.

AI doesn’t need to be perfect. But it must be trustworthy. If we draw clear boundaries, teach with care, and build monitoring into the loop, we can let AI scale up — safely and with integrity — rather than racing ahead blind.

Actionable Insights

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miles is the distance Lithuania’s Aleksandr Sorokin ran in just 24 hours in Italy, setting a World Record that pushed the boundaries of human stamina and determination.