Amir Zandieh, Research Scientist, and Vahab Mirrokni, VP and Google Fellow, Google Research
Ultra-Processed Information: AI and the Coming Deluge of Noise | Frankly 128
Unfortunately, You Need to Know What the Jevons Paradox is
Unfortunately, You Need to Know What the Jevons Paradox is
TheStage AI – Faster, Cheaper AI Inference
Accelerate models on NVIDIA & edge. Full guides for setup, optimization & deploy. ANNA, QLIP, Elastic Models, CLI & API. Built for AI teams & devs.
The Optimization Trap: Why Too Much Efficiency Makes Us Fragile with Olivier Hamant
Together AI | The AI Native Cloud
Build what's next on the AI Native Cloud. Full-stack AI platform for inference, fine-tuning, and GPU clusters — powered by cutting-edge research.
Hyperfast AI: Rethinking Design for 1000 tokens/s
I recently spoke at AI Tinkerers Raleigh about hyperfast inference systems and how they’re fundamentally changing AI application design. If you haven’t heard of Cerebras (or however they pronounce it), you’re in for a treat—this is one of the most exciting areas of research in AI right now.
AI's Trillion-Dollar Opportunity: Sequoia AI Ascent 2025 Keynote
AI coding agents generate more code, but not more software
Study finds coding efficiency gains get "absorbed" by human review "bottleneck."
AI Isn't as Powerful as We Think | Hannah Fry
Research acceleration: The view inside OpenAI
Inside OpenAI, coding agents are reshaping AI research. Explore early data on agent usage, experiment velocity, task complexity, and research acceleration.
Alex Cheema on Twitter / X
This is why we need open benchmarks for local AI.Otherwise it turns into tribalism and name calling.We will be publishing the largest database of open benchmarks for local AI, tested on 1,000+ real hardware setups. Every device, every interconnect, different… https://t.co/ZsU3PCdSsZ— Alex Cheema (@alexocheema) March 9, 2026
The Bitter Lesson: Rethinking How We Build AI Systems
The Race for AI Progress In 2019, Richard Sutton, wrote his groundbreaking essay titled ‘The Bitter Lesson’. Simply put, the essay concludes that systems which get better with higher compute beat the systems that do not. Or specifically in AI: raw computing power consistently wins over intricate human-designed solutions. I used to believe that clever orchestrations and sophisticated rules were the key to building better AI systems. That was a typical sofware dev mentality. You build a system, look for edgecases, cover them and you are good to go. Boy, was I wrong.
AI & Alignment Raw coding speed isn't the bottleneck. Alignment is the bottleneck. That seems to be a zeitgeist-y theme lately. If you're using AI to code, maybe you're feeling it. You can code more and faster. And clearly a boatload of other developers are doing that too. But software doesn't…
AI & Alignment
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