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Hasan Faisal

AI can unlock bolder research paths and make discovery more accessible.

What strikes me most isn't the pace of technological progress, it's the pace at which leadership capability needs to evolve alongside it. Building AGI may be the defining technical challenge of our era. Building organisations capable of deploying it responsibly, with the right leaders, culture and judgment at every level, may prove equally demanding. The bottleneck won't be the technology. It rarely is. The real challenge will be ensuring human judgment evolves as quickly as machine capability. Fascinating perspective, Demis.

Adoption tends to accelerate when the technology solves a specific decision or productivity problem.

Joanne Bell 100%

Many thanks for sharing

Won't download

Carter Franz the tec industry is making us participate in many 'why should I?' Why should the tax payer's be sponsoring SpaceX, when the survival of earth is measured in millions of years and not hundreds?

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Incredible progress, especially on agentic systems. Building production GenAI platforms in regulated environments taught me that capability is only half the problem. The harder challenge is governance around what agents actually do: authorization, grounding, audit trails, and the ability to intervene mid-execution. The SynthID adoption is also quietly the most important announcement here. Provenance becoming a cross-industry standard before widespread harm occurs is genuinely rare in tech history. The foothills of the singularity is a striking framing, and what gives me confidence is that the people building at the frontier are treating safety as foundational, not an afterthought.

Mind-blowing pace! 🤯

Clear, actionable, and well articulated

Interesting context: AI Is Almost Green Compared To Netflix, Zoom, YouTube https://www.forbes.com/sites/johnkoetsier/2025/12/03/new-data-ai-is-almost-green-compared-to-netflix-zoom-youtube/

AI just started to build its own AI in my toaster. It then created a protocol called the dough protocol. It built a robot out of my dishwasher. It's stealing all of my dough

Using the biggest model for every task is starting to feel like taking a Ferrari grocery shopping when you have a great gas-mileage minivan in the driveway. What’s interesting here is the architecture choice: specialized models, routing, aggregation, and only spending expensive compute when the task actually earns it. For businesses, the metric that matters is not “which model is smartest?” It is cost per reliable outcome. ELM feels like a step in that direction.

Great share Abhishek Veeramalla

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The ensemble approach is underexplored in the LLM space. Most of the industry defaulted to scaling monolithic models but the compute efficiency argument here is compelling. Routing distinct specialized models through an orchestrator rather than forcing one model to do everything mirrors how we think about mixture-of-experts at a higher abstraction level. Curious how ELM handles the coordination overhead and latency trade-offs across ensemble members.

AI does not know until we tell it. Whether it be context, problem statement , situation, priority or risks. It is important to provide complete statements. Also any of the factors of context might get dropped by AI systems as the conversation progresses. But, isn't that a problem with common human judgements as well ?

The 4-month doubling cycle on autonomous task length is the stat that should be getting more attention. That's a faster feedback loop than most governance frameworks are built to handle.