🤖 LeCun Bets Against LLMs — AI pioneer Yann LeCun is launching AMI Labs in Paris, calling the industry's obsession with Large Language Models "wrong-headed." Instead, AMI will build "World Models" that understand physical reality and cause-and-effect, aiming to create open-source systems capable of true reasoning and planning.

🤖 US States Defy AI Deregulation — In a major 2026 showdown, states like California and New York are defying a new White House executive order designed to block local AI regulation. With over 1,000 bills introduced, states are fighting to keep safety laws for chatbots and data centers alive despite federal threats to cut funding.

🤖 China Quietly Winning AI Race? — Major US companies like Pinterest and Airbnb are shifting to Chinese AI models such as DeepSeek and Alibaba’s Qwen, reporting they are 30% more accurate and up to 90% cheaper than US alternatives. Stanford data confirms Chinese open-source models now dominate download charts.

🤖 Smartphones Locked Away — Artists like Paul McCartney and over 500 UK schools are now using magnetic locking pouches to enforce strict "phone-free" zones. While the devices aim to curb bullying and "FOMO," proponents argue that physically locking away devices is the only way to reclaim human connection from algorithms.

📌 Gen Z's AI Takeover — The average age of AI "unicorn" founders has crashed from 40 to 29 in just four years. Driven by "AI-native" intuition, founders in their 20’s are raising millions, though veteran entrepreneurs warn this speed often masks fragile foundations—specifically a lack of leadership experience and established professional networks.

📌 Articulating Senior Impact — For senior leaders, the challenge isn't doing the work, but justifying your salary without stealing credit. The solution is the "We-then-Me" framework: praise the team's execution first, then pivot to your specific strategic judgment—the tradeoffs, risk management, and relationship-building that made the win possible.

📌 AI in Hiring: Worse, Yet Essential — AI was supposed to democratize hiring, but instead, it has created an "arms race of automation," flooding employers with artificially polished, AI-generated candidates and fake avatars. To fix this "crisis of trust," companies must stop using AI to replace humans and start using it to enforce structure.

Today's opportunities are brought to you by RightSide.

Associate Program Manager | Full Time | Greene, NY | In Person
ITSM ServiceNow Consultant | Contract | P2PSoftTek | Remote
IT Service Technician | Full Time | New Orleans, LA | In Person
Automation Tester (Insurance) | Full Time | Edison, NJ | Hybrid
Mobile Engineering Lead | Full Time | Atlanta, GA | Hybrid
Oracle Technical Specialist | Contract | Rockville, MD | Hybrid
VP Data Center of Excellence | Full Time | QualiTest | Remote

Large language models have dominated the AI landscape in recent years, powering chatbots, assistants, and generative tools in everything from writing to coding. Yet researchers and industry leaders increasingly recognize that LLMs alone won’t solve every AI challenge — especially those requiring real reasoning, real-world action, and efficient computation. This has sparked interest in new AI paradigms that go beyond traditional transformer-based language models, seeking greater efficiency, better reasoning, and more adaptive behavior. One emerging example is the “energy-based” reasoning model Kona, which has been touted as outperforming leading LLMs on certain logic and structured problem tasks while consuming less computational energy — a potentially important step toward more efficient, reliable AI systems.

One of the most active areas of innovation is agentic AI systems — models designed not just to respond to prompts but to act, plan, and reason autonomously within dynamic environments. Surveys of agentic AI architectures highlight how these systems combine elements like planning, memory, and tool use into cohesive frameworks, enabling AI that orchestrates multi-step workflows and adapts goals over time rather than simply generating text in response to queries. These architectures aim to bridge the gap between static LLM behavior and truly interactive, adaptable AI capable of complex algorithmic and decision-making tasks.

Another growing trend is hybrid and multimodal models that integrate reasoning, perception, and action in ways traditional LLMs cannot. For example, Vision-Language-Action (VLA) models like Helix are being developed to combine language understanding with real-time control of robotic movement, allowing AI to interpret scenes and act physically in real environments. Compact versions such as SmolVLA make this kind of embodied intelligence more accessible, hinting at a future where AI models don’t just generate text but interact with the world around them.

Finally, other innovative alternatives include neuro-symbolic architectures and compound AI systems that integrate retrieval, reasoning, and modular components into coherent workflows. Neuro-symbolic approaches marry statistical pattern recognition (typical of neural networks) with structured, logic-based reasoning, addressing known limitations of purely associative learning systems. Meanwhile, Compound AI Systems — which layer tools, retrievers, and orchestrators around neural models — expand capability beyond stand-alone LLMs by enhancing memory, grounding, and task execution. These emerging paradigms suggest the future of AI will be less about scaling a single giant model and more about combining specialized intelligences and architectures to achieve robust, efficient, and context-aware behavior.

31

years old is the median age of the global population in 2026—a significant jump from a median of just 22 years old in 1970.

Know an AI enthusiast? Share the value. FORWARD this email!