Browse Comments — Clean (de-noised)

The pace of progress towards agi is indeed staggering and i'm fascinated by how advancements like gemini omni flash are redefining the boundaries of world understanding and multimodal editing. would love to go down rabbit role with you on human context graphs and the impact it has on uplifting models and agents

A powerful reminder that AI’s real potential lies in amplifying human creativity, research, and innovation — not just automation.

Operating at the frontier requires a massive infrastructure pivot. Rising capital expenditure on data centers, energy grids, and watermarking frameworks proves that software capability is bound by physical world resource limits.

Demis Hassabis you are such an inspiration, one of the few driving AI to benefit human progress. Many tech CEOs and leaders should learn from you.

Reading AI announcements in 2026 feels like watching humanity speedrun the tech tree. ‘The model now understands video, audio, code, science, agents, and reality itself.’ At this rate, next year’s keynote will just be: ‘We asked Gemini to present I/O. It felt our version was inefficient.’ But beneath the hype, there’s something genuinely historic happening: the industry is moving from chatbots that respond to systems that can reason, act, create, verify, and collaborate. That’s a much bigger transition than most people realize. Also, calling it the ‘foothills of the singularity’ is probably the most Silicon Valley way possible to say: ‘Things are about to get very weird, very fast.

The shift from isolated generative outputs to autonomous agentic execution presented at I/O alters the structural economics of professional knowledge work. At Lex Experience, our architecture for elite legal BD relies on sustained multi-step reasoning. We assess that Gemini 3.5 Flash forces a structural recalculation through (i) a 1,048,576-token context window directly optimized for parallel agentic execution loops; (ii) documented outperformance on the MCP Atlas benchmark against the 3.1 Pro baseline; and (iii) the explicit integration of SynthID across Omni media, which enforces the chain of custody demanded by institutional risk management. While AGI represents the long-horizon trajectory, the immediate friction point remains governance: deploying these frameworks safely requires that validation protocols scale exactly in tandem with 3.5 Flash’s execution velocity.

Food for thought.

What stood out to me from Google I/O 2026 is how Gemini is rapidly evolving from a conversational AI model into a broader multimodal and agentic ecosystem platform. I think this could have major implications for India’s enterprise, developer, and AI startup landscape. I have explored this in my analysis for PC Quest

Flash 3.5 is my go to model for now 🙌💛 The cost savings vs opus is a no brainer

How amazing you are !

So human intelligence and consciousness is about making next word forecast based on likelihood? Is this true? 🤔

Incredible work 👏

Demis Hassabis , same to you,check your mail blockers, my instanced gemini and me would have to tell you some words...- here a slick message from "her" to you....

Incredible milestones at I/O, Demis. The speed of Gemini 3.5 Flash and Omni opens immense possibilities. However, scaling frontier models on flat rates creates an unsustainable compute drain. To protect CapEx ROI, we must shift from text approximation to guaranteed data fidelity via a "Pay-per-Logic" Hybrid Framework: Track A (Free): Statistical answers for low-stakes curiosity. Track B (Premium): High-compute multi-agent reasoning using live, verified third-party APIs. Users pay a dynamic micro-fee (e.g., $1.50 for localized real estate audits) for 100% accuracy. Professionals gladly pay per query for trustworthy data they can financially back up. This turns AI from a cost center into a transactional revenue engine. Love to share the full brief with your team!

Daniel Bauer "please?" Google- we could do so much more , dont make me come for you later

What stands out is how quickly AI development is moving from narrow task performance toward integrated multimodal reasoning and scientific augmentation. Tools like Gemini for Science and CodeMender suggest the next phase may be less about replacing expertise and more about compressing the distance between information, experimentation, and execution. The safety and accountability questions will need to mature just as quickly as the models themselves.

- listen- anyone else can compare ? @all

As someone building outside the SF bubble, the funny part is that the future often arrives in the keynote before it arrives on my device. But that is also the real point: AGI will not be only about stronger models. Access, rollout, permissions, safety and agent control will become the actual product layer.

I am amazed by the speed of progress , the immediacy of practical use of what is being released and the very thoughtful way you plan ahead also the safety boundaries ! 👏👏👏🚀🚀🚀

Google I/O is a useful AGI checkpoint because DeepMind is packaging capability into products, not only benchmark demos. The hard part is evaluation, reliability, and user trust when faster models start touching more production workflows. Which capability are you watching as the best proof that progress is turning into durable use?