Browse Comments — Raw (as collected)

Most teams will underestimate the integration layer here. It’s not about adopting models, it’s about rethinking data access, permissions, and auditability before agents can operate at scale in real environments.

The gap between I finished the course and I can build this in production is where most developers get stuck. Resources like this that show complete working systems not just isolated code snippets are genuinely rare. As a Full Stack Dev this is going straight into my learning roadmap. Appreciate you and Oracle for making this accessible! 🙏

Adj. Prof. Dr. Behrang (Hani) Parhizkar good that UAE can pilot agentic at this scale, for the rest of the world to see the teething issues and learn from there. That’s exactly what happed with Real Estate Asset Tokenization in UAE, where most token holders are stuck, because real estate in UAE tanked 35% over night. Technology can only solve so much, the business fundamentals needs to be right ultimately…

Evan Hunter Evan the nuclear analogy lands hard because it's exact. We didn't let private companies self-regulate fissile material and call it innovation. The distinction that made nuclear different was that governments understood the catastrophic downside before widespread deployment — not after. The governance infrastructure preceded the technology at scale. AI governance is running in reverse. The technology is deployed at scale while the governance infrastructure is still being argued about in congressional hearings. Your point about C-suite and board accountability preceding vendor accountability is right — the problem is most boards don't yet have the technical literacy to ask the right questions. Which creates a gap that vendors fill with their own risk framing. The Vatican moment matters because it's one of the architects saying publicly that the internal accountability structure is insufficient. That's the signal boards should be acting on right now.

Chicago’s Best. Al Capone and Pope Leo.

This is the kind of resource that shortens the learning curve massively. A lot of people say they want to learn AI… but they stay trapped in tutorial loops. Watching videos. Saving threads. Never actually building. What stood out to me here is the focus on production-ready systems.

Demis, we are slowly moving from “AI that responds” to “AI that acts.” And that changes everything. Because once systems can reason + execute, the real challenge is no longer intelligence, it’s control, accountability, and trust in real-world actions. AGI is not just a capability milestone anymore… It’s becoming a systems design problem.

there’s no effective leadership without emotional intelligence. it’s an emotional game

The transition from 'Scaling' to 'Ensembles' is the reality check the industry needs. Orchestrating hundreds of specialized models for a fraction of the cost is true technical maturity.

👏🏻

This is only true if you let technical leaders that don’t understand AI implement your systems 😂🤣

Universal morality is bare bone basics 💀

This is one of the simplest and smartest explanations of modern AI architecture I’ve seen. Understanding the difference between LLMs, RAG, AI Agents, and MCP is becoming essential in today’s AI-driven world. The comparison with the human body makes it much easier to understand. Great post!

I just think its scary that Elon Musk will look like Adam Savage in the future

Gemini Omni, Gemini for Science, CodeMender, and SynthID all point to the same direction: AI systems that can understand the world, act across workflows, accelerate research, secure code, and still leave room for trust and provenance. Feels like the real race now is not just capability, but responsible deployment at scale.

Matthew P. Your point about AI governance running in reverse is well made. Thank you for clarifying. I would push back on the idea that the potential catastrophic downside risk inherent to AI was well understood prior to its use at-scale (by DARPA, The Pentagon, Science Fiction writers, Hollywood, etc). However, enough decision makers (public and private) were sold on the following: 1) massive upside of digital assistants (productivity, prosperity, innovation) 2) long time-horizon on generative ai, wide-spread adoption, and digital sentience and so here we are. I hope that boards will act on that signal, but am not yet optimistic. A lot of big egos are going to have to walk back pet projects and big promises to Constituents, Congress, and Wall Street, and sadly, that seems unlikely to be a swift process absent tight regulation - which, will almost certainly be viewed as reactionary and overly-restrictive.

AI should be used to look inwards not outwards

What stood out most from I/O this year is how much of what DeepMind showed has moved from research milestone to deployable product. The staggering pace Demis references is real — but what's more remarkable is that the deployment lag is shrinking just as fast.

The real concern isn't just what AI can do, it's how the power dynamics shift. I've seen tech solutions that could help millions but end up benefiting just a handful of people, Pascal. If we don't address this, we risk repeating history where innovation creates more inequality.

Great job Very necessary for beginners to have this