Tag: AI

AI trust is collapsing. OpenAI, NVIDIA, generative AI, and Big Tech are spending hundreds of billions while public trust in artificial intelligence keeps falling.
AI was supposed to become the next great leap forward. Instead, people are watching advanced models pass elite exams, then fail simple tasks like reading a clock, counting letters, or separating fact from hallucination.

Behind the hype is a growing problem. AI can look brilliant in one moment and completely unreliable in the next. That “Jagged Frontier” is shaking public confidence, even as Silicon Valley keeps pouring money into data centers, chips, power grids, and infrastructure.

From AI hallucinations and Apple’s research, to the Stanford AI Index, environmental costs, public backlash, and fears of an AI bubble, this is why trust in AI is collapsing.

Why does Silicon Valley believe so strongly in a technology the public still doesn’t trust?

I’m back from Google I/O, and while everyone saw the big keynote announcements, some of the most interesting AI tools were buried in the margins.

In this one, I dig into what Google is actually building around Omni, Flow, Genie, video editing, world models, audio tools, and the creator workflows that did not get the clean keynote headline. I also got to talk with Josh Woodward and Logan Kilpatrick about what Omni is, where video fits into it, and why this might be less “one new video model” and more “a new layer for editing, remixing, compositing, and building with AI media.”

The short version: Google’s AI video plan is getting weird. Maybe underpowered in spots, definitely sprawling, but also way more interesting than just waiting for the next model name.

Plus — for the first time ever — a loose, on-the-ground podcast with some of my favorite creators recorded straight from the I/O floor.

I’ll show you how to use Zentangle keywords to create high-contrast ink masterpieces and why the Celestial style is the secret to creating ethereal, “space-photography” gods. We also explore the Grid Layout (a sophisticated evolution of Knolling) using the city of Buenos Aires as our architectural backdrop, and I reveal the technical prompts needed to create the high-energy Shattered effect on materials like porcelain and skin. Finally, we look at the kaleidoscopic symmetry of DMT Art for the ultimate visionary aesthetic.

Neil Patel explores a significant shift in the AI landscape, highlighting that the CEOs of OpenAI, NVIDIA, Google, Microsoft, and XAI are all signaling the same transition for 2026: the move from AI models to AI systems and autonomous agents.

The Core Shift: From Search to Agents

Patel explains that the industry is moving away from users manually searching for information and toward “AI agents” that perform complex, multi-week tasks autonomously.

• Intelligence as a Utility: Sam Altman (OpenAI) suggests AI will soon be as accessible and essential as electricity or water.
• Infrastructure over Apps: Jensen Huang (NVIDIA) notes that AI has moved from training to “productive work,” requiring a massive infrastructure takeover.
• Agent Orchestration: Sundar Pichai (Google) describes the future of search as an “agent orchestrator” that completes tasks rather than just providing links.
• Systems over Models: Satya Nadella (Microsoft) emphasizes that the real impact comes from integrated systems, not just standalone LLMs.

What This Means for Marketing

The audience for digital content is changing. Instead of writing for humans who skim pages, marketers must now optimize for AI agents that dissect, extract, and cite information.

• The New SEO (GEO/AEO): Traditional SEO is evolving into Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO).
• Citation is the New Ranking: The goal is no longer just to “rank #1” on Google, but to be the source an AI agent trusts and cites when answering a user’s prompt.
• High Conversion: Patel shares data showing that while AI platforms currently drive less traffic than Google, that traffic has significantly higher purchase intent and revenue conversion.

3 Actionable Strategies for 2026

1. Build for Citation: Structure content to be machine-readable. Use clear attribution, structured data, and direct answers upfront so agents can easily extract your insights.
2. Look Beyond Google: With over 70% of searches now happening across platforms like YouTube, TikTok, ChatGPT, and Perplexity, brands must ensure visibility across the entire “research layer” of the internet.
3. Develop Systems, Not Tactics: Move away from using AI as a one-off tool (like just writing a blog post). Instead, create feedback loops where content production is measured by agent retrieval and citation frequency to constantly improve strategy.

Patel concludes that the window for early-mover advantage is closing, and marketers who adapt to this “agent-driven” economy now will build a competitive moat that latecomers will struggle to cross.

Google just dropped a wild new AI video model that lets you insert yourself into any scene you can imagine. And it’s available right inside Gemini.

Current AI technology seems to be making decent progress despite concerns about it slowing over time. But while AI is slowly becoming more “intelligent”, the industry is running into another problem: energy supply. Let’s take a look at why energy is quickly becoming a major problem for progress in AI.

The Google I/O Keynote highlights the transition into an “agentic era” of AI, focusing on foundational infrastructure, advanced multi-modal models, personal AI agents, and deep scientific applications.

1. AI Models & Developer Infrastructure

• TPU 8th Generation (AT & ATI): Google introduced specialized custom silicon for training and inference. The training architecture allows Jackson Pathways to distribute workloads across multiple sites, scaling past 1 million TPUs globally [12:15].
• Gemini Omni & Gemini 3.5 Flash: Demis Hassabis introduced Gemini Omni, a world model capable of simulating physical principles like kinetic energy and gravity to generate highly realistic, editable video across inputs [17:20]. Concurrently, Gemini 3.5 Flash launched, optimized to deliver fast, highly cost-efficient agentic actions and coding workflows [23:45].
• Google Anti-Gravity 2.0: A standalone, agent-first desktop application designed for multi-agent orchestration. In a benchmark test, autonomous sub-agents collaborated to build a functioning operating system from scratch within 12 hours for under $1,000 in API credits [28:12].

2. Reimagined AI Consumer Products

• Google Search Evolution: Google search box has been completely upgraded [46:58]. Enabled by Gemini 3.5 Flash and Anti-Gravity, Search can now deploy live containerized code to create real-time custom layouts, interactive visuals, and continuous dashboards (e.g., personalized weekend trackers) on the fly [51:50].
• Gemini App & Neural Expressive Design: The Gemini App has over 900 million monthly active users and has received a fluid, interactive interface upgrade called “Neural Expressive” [01:10:39].
• Gemini Spark: Announced as a persistent personal AI agent running 24/7 on dedicated cloud VMs [35:27]. Spark securely integrates with your inbox, tools, and third-party apps via Model Context Protocol (MCP) to manage heavy background tasks under user boundaries and supervision [01:16:06].

3. Commercial & Shopping Ecosystems

• Agent E-Commerce Building Blocks: Google unveiled the open-source Universal Commerce Protocol (UCP), co-developed with partners like Amazon, Meta, and Stripe, to standardize transaction communications [59:44].
• Agent Payments Protocol (AP2) & Universal Cart: AP2 securely delegates bounded, accountable financial limits to AI agents [01:01:17]. This operates alongside a cross-merchant “Universal Cart” that checks cross-retailer inventory, points out missing hardware incompatibilities, and sources hidden credit card savings automatically [01:03:07].

4. Interactive Media & Hardware Ecosystems

• Creative Tools: Google Pix allows contextual image editing and formatting [01:24:18], Stitch handles prompt-to-UI designs [01:25:33], and Google Flow / Flow Music empowers video asset variations and R&B audio generation [01:28:22].
• Intelligent Eyewear (Android XR): Built in partnership with Samsung, Qualcomm, Warby Parker, and Gentle Monster [01:35:21]. Google announced display glasses alongside audio glasses coming this fall [01:33:40]. These glasses stream hands-free map navigation, read notifications, and utilize background apps (e.g., ordering coffee via DoorDash) straight into your ear [01:40:51].

5. Security & AI for Science

• Synth ID Scaling: To handle deepfakes and transparency, Google expanded invisible watermarking. It is natively integrated across Chrome, Google Search, and adopted by industry partners like OpenAI and ElevenLabs [21:04].
• Gemini for Science & Weather Next: DeepMind continues targeting drug discovery and planetary simulation. Its updated climate model, Weather Next, accurately predicted a Category 5 Jamaican hurricane 3 days early, demonstrating the potential of scaling digital twin technologies to solve real-world crises [01:47:37].

Dr. Errol Brandt evaluates the current state of artificial intelligence, contrasting the prevalent cloud-based infrastructure with Apple’s localized hardware strategy. This analysis explores the economic challenges facing current AI business models and examines the potential impact of future hardware developments on the industry’s reliance on centralized data centers.

Google I/O 2026 was all about improvements in AI. Gemini is getting smarter, faster and more customized. See what was presented at this year’s event, from a more intuitive Gemini Omni to the realization of Android XR with consumer products coming in the fall. We’ve got the highlights.

Google Omni is here, and I got a few days of early access before Google I/O. In this one, I test Google’s new Omni video model across text-to-video, image-to-video, video editing, style transfer, clip extension, avatar/cameo mode, lip-sync repair, camera-angle changes, POV shifts, and full location changes. Google is pitching Omni as the next step after Nano Banana: a multimodal Gemini model that can create video from images, text, video, audio references, and natural language instructions. The interesting part is not just generation. It is conversational video editing, multi-turn refinement, consistent characters, scene memory, style changes, and using Gemini’s world knowledge to make complex ideas visual. My early takeaway: Gemini Omni Flash is not really a Seedance killer yet. If anything, it feels more like the start of Nano Banana for video — less about one perfect text-to-video clip, and more about using AI to remix, repair, restyle, extend, and reimagine video through conversation. It is early. It is definitely not perfect. But if Omni develops the way Nano Banana did for image generation and editing, this could become a much bigger deal than a normal AI video model launch