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In this episode I compare the future of AI to the failure of the music industry in the early 2000’s.

In this tutorial, I show you step by step how to generate a full 9-panel ai storyboard from a single reference image. You’ll understand why generating all scenes as one image keeps everything consistent thanks to the self-attention mechanism, and how to turn each panel into animated clips Google VEO 3.1. The full process from idea to finished video takes under 10 minutes with zero regenerations.

I also share an advanced “bookend” technique where you define the first and last frame and let the AI fill in the middle panels. This gives you full creative control when you need a specific character arc or transformation for your how to make storyboard for video editing projects.

ChatGPT just got sued for inventing fake court cases. Claude just got banned by the US government. Hours after the ban, that same AI was used to select targets in a military operation, marking AI’s first conflict. And now ChatGPT, the one that hallucinates court cases, will replace Claude gaining access to classified military intelligence.

Copilot was supposed to be Microsoft’s next big leap. A PC you can talk to. A new category of computing. Instead, it’s everywhere and barely anyone wants it. Out of 450 million Microsoft 365 commercial seats, only about 3 percent are paying for Copilot. Even though Microsoft rebranded apps, pushed it into Windows 11, Edge, and Office, and spent tens of billions on AI infrastructure, adoption never matched the hype. Nadella compared it to the PC, the web, mobile, and cloud. That’s a wild claim for a tool most companies are still “piloting.” Forrester projected massive ROI. In reality, only a tiny fraction of organizations rolled it out company wide. Users complain it gives instructions instead of actually doing the task. Developers often prefer ChatGPT or Claude. Even Microsoft employees reportedly use other tools. So Microsoft pivoted. Copilot got bundled into higher priced personal plans. In some regions, customers were pushed into upgrades. Regulators noticed. Copilot might work in narrow coding use cases. But as the future of Windows and enterprise productivity? That dream looks a lot smaller than Microsoft expected.

Everyone thinks AI is responsible for the wave of tech layoffs. The reality is more structural: the era of cheap capital ended.

For almost a decade, near-zero interest rates pushed investors up the risk curve. Venture capital flooded the tech ecosystem, companies hired aggressively, and organizations added layer upon layer of management, coordination roles, and internal complexity. When money was essentially free, few companies had to ask the hardest question in business: who actually creates value here?

Now that capital is expensive again, that question has returned.

In this video, I tell the story of a former Amazon director I met on a ski lift at Mt. Bachelor who was recently laid off. At first glance, his story sounds like the narrative everyone is hearing right now: AI replacing workers. He had built an automation system using AI that could re-engage tens of thousands of small business accounts.

But when you look closer, the automation itself wasn’t new. Systems like this have existed for more than a decade in tools like Eloqua and Marketo. The real reason he lost his job wasn’t AI—it was a return-to-office policy.

His story reveals a deeper pattern inside large tech companies.

During the cheap money era, companies massively expanded headcount. Amazon doubled its workforce between 2019 and 2021, while revenue per employee dropped significantly. Organizations grew more political, more risk-averse, and slower as layers of internal coordination multiplied.

Now that capital is expensive again, those layers are being cut.

Economists call the outcome a K-shaped economy: after disruption, some people move upward while others fall behind. The dividing line is not intelligence or talent—it’s proximity to value creation.

People who own processes that generate revenue, protect margins, control distribution, or build systems move upward. People whose roles depend on organizational layers, titles, or presence inside a company become vulnerable.

The real shift happening in the economy is the collapse of abstraction—the distance between the person doing the work and the person paying for the value is shrinking.

AI is accelerating this trend, but it isn’t the root cause.

The real question every professional should ask is simple:
Do you own a process that directly produces revenue or protects margin?

Because the future belongs to the people who do.

Nano Banana 2 is changing AI filmmaking. It’s the first ever AI image generator able to research the real world while generating your frame, thanks to built-in Google Search and Image Search grounding. In this video we show how filmmakers can use Nano Banana 2 to create accurate, believable and consistent cinematic frames instead of random AI hallucinations. You will learn practical AI filmmaking workflows that allow you to: • reconstruct real historical locations and weather conditions • generate accurate objects and props • swap elements inside the same shot • localize scenes for global markets • build full cinematic coverage from a single master shot Instead of generating random images, Nano Banana 2 allows you to direct scenes like a filmmaker – controlling locations, props, lighting and continuity. This is not a basic prompting tutorial. This is a practical masterclass for AI filmmakers who want to turn image generation into a real directing tool. If you want to build credible cinematic worlds with AI, this video will show you how. WHAT YOU WILL LEARN • AI filmmaking workflows with Nano Banana 2 • Google Search grounding for historical accuracy • Image Search grounding for realistic props and objects • Element swapping and scene editing • Global scene localization • Master shot construction and shot coverage • Cinematic continuity using AI image generation TOOLS DISCUSSED Nano Banana 2 Gemini AI AI filmmaking workflows AI video pre-production techniques

As generative AI upends industries around the world, the creators of Japan’s popular manga comics are debating whether the technology is a threat or opportunity.

Some think AI can help with labour shortages and boost productivity, but many artists and publishers fear copyright infringement, falling incomes and the devaluation of human artistry.

In this episode of Asia Specific, host Mariko Oi speaks with a Tokyo-based manga artist Peppe, AI consultant Darren Boey and Takeshi Kikuchi from the Manga Research Institute about how AI is changing this popular art form.

Say Goodbye To Plastic AI Skin | Introducing Vellum By TheCluelessAI. Vellum is a high-fidelity AI skin model developed by Clueless AI and now available exclusively on OpenArt.

Dave Rubin of “The Rubin Report” shares a DM clip of Elon Musk explaining to Peter H. Diamandis how AI and robots will likely lead to a universal income in the future.

LTX Desktop just dropped — a free, open source, fully local non-linear video editor built on the LTX 2.3 engine. Today we’re going through the whole thing: how to install it, what it can do, what it can’t do, and why I think this matters more than most people realize.

We’re also running through the LTX 2.3 model updates including the rebuilt VAE, image-to-video fixes, native portrait video, and audio quality improvements.