AI in Creative Industries

I couldn’t be more excited to share our latest AI research breakthrough in video generation at Meta. We call it Movie Gen and it’s a collection of state-of-the-art models that combine to deliver the most advanced video generation capability ever created. Movie Gen brings some incredible new innovation to this field including: • Up to 16 seconds of continuous video generation – the longest we’ve seen demonstrated to date. • Precise editing – unlike others that are just style transfer. • State-of-the-art video conditioned audio which is better than all the text to audio models • Video personalization in a way never done before – not image personalization with animation. We’ve published a blog and a very detailed research paper along with a wide selection of video examples that you can check out: https://lnkd.in/gTfwRsHm

AI Fingerprints Found in Millions of Scientific Papers, Study Reveals Introduction: The Quiet Rise of AI Authorship in Academia As large language models (LLMs) like ChatGPT and Google Gemini become increasingly capable of producing high-quality writing, their influence is now visibly permeating academic literature. A massive study analyzing over 15 million scientific papers has uncovered measurable linguistic patterns that suggest a significant portion of biomedical research may already be shaped—at least in part—by artificial intelligence. Key Findings from the Study • The Scope of the Analysis • Conducted by U.S. and German researchers, the study focused on biomedical abstracts published in PubMed—one of the largest databases of peer-reviewed life sciences literature. • Researchers used AI-detection techniques to track stylistic patterns and word choices consistent with LLM-generated or LLM-assisted writing. • AI’s Growing Influence in Scientific Writing • The results show that at least 13.5% of scientific papers published in 2024 were likely produced with help from a large language model. • Since the emergence of tools like ChatGPT, there has been a marked increase in specific phrasing and terminology that mirrors LLM outputs. • These “AI fingerprints” suggest that AI-generated or AI-edited content is becoming increasingly normalized in academic publishing. • Implications for Research Integrity and Peer Review • The widespread use of LLMs in academia raises questions about authorship transparency, originality, and peer review standards. • Editors and journals may need to establish disclosure protocols and develop more robust AI-detection tools to maintain the integrity of published work. • While AI can assist with grammar and structure, overreliance could blur the line between assistance and authorship. Conclusion: Rethinking Scientific Writing in the AI Age This study provides compelling evidence that AI is no longer a background tool in academic research—it’s rapidly becoming a co-author. As scientific publishing adapts to this new reality, the challenge will be to harness AI’s efficiency without compromising intellectual integrity, accountability, or the human creativity that drives true discovery. https://lnkd.in/gEmHdXZy

For the first time, Nano Banana’s AI agent can design across all mediums: images, video, 3D, music, even voiceovers. Honestly, this makes me rethink my entire view of creativity. I used to believe design was defined by tools—the brush, the camera, the editing suite. But if one agent erases those boundaries, then what even is a “designer” anymore? I catch myself wondering: will future creatives introduce themselves not by their craft, but by their perspective? In my opinion, this isn’t just another creative tool—it feels like the beginning of a new design era. I’ve spent years switching between Photoshop, Premiere, Blender, and Ableton. Suddenly, all of that fits into a single agent. It’s thrilling… and unsettling. Here’s what it means: → Creative disciplines are converging. → The barrier to professional-level work is collapsing. → The role of “designer” itself may be redefined. Here’s how I think we can stay ahead in this new landscape: ✅ Focus on taste and judgment—the one thing AI can’t replicate. ✅ Build multi-modal fluency: don’t just know visuals, learn sound and interaction. ✅ Treat AI as an amplifier, not a replacement—bring in your unique point of view. The real shift is this: creativity is moving from production to direction. From “how do I make this?” to “why should this exist?” And that, to me, is both exciting and terrifying—because it means the creative field is no longer about tools. It’s about judgment. 👉 So here’s the debate: will this new wave of AI usher in a golden age of creative abundance—or a race to creative sameness? #AI #Creativity #FutureOfWork #Design #Innovation Video credits: x.com

If I were starting as a designer in 2026, here's what I'd do: Most designers are optimizing for the wrong things. Being good at Figma is no longer how you stand out. AI will become better at polishing UI than most designers anyway. What matters now is knowing what to build, recognizing product quality, and articulating intent clearly enough that AI becomes your best sparring partner. That's the new designer skill set worth betting on in 2026. If you have clear thinking, good visual taste, and judgment about what to build, you'll win. When anyone can execute at a baseline level, your product mindset becomes your only differentiator. So here's the thing: developing that mindset is way harder than developing technical skills. But no one is born with it, it all comes through practice. You need to ship and ship and ship until you get really good at it. So if I were starting today, here's what I'd focus on: 1/ Develop your product mindset before pro tool skills Study great products obsessively. Ask why things work. Build your internal quality bar for what good looks like. 2/ Learn to articulate your intent clearly AI execution is only as good as your clarity of thought. Practice describing what you want in specific, unambiguous language. Prompting is the new wireframing. 3/ Ship constantly, not perfectly Excellence comes through repetition. Build 10+ versions of something rather than perfecting one in isolation. 4/ Understand the full stack enough to be effective You don't need to code, but understand how AI systems connect. Take advantage of MCPs, agents, and automations to build end-to-end experiences. 5/ Develop strong opinions Your value isn't speed. It's knowing what's worth building and recognizing quality when you see it. You'll win as a designer by developing clear vision and sharp taste, then using AI to execute at impossible speed. The golden age of the design founder is here. But only if you're developing the right muscles.

As a CMO, one of my top priorities right now is working out what role AI will play in our marketing work at Google. In my experience, Creatives are always the first to play with new tools, and AI is the most exciting sandbox yet. I believe this moment could be a fundamental shift in how we create, allowing us to have impossible ideas and to do things we never could before. While it is still early days, we are already seeing AI revolutionise our workflows, whether it's saving us countless hours storyboarding with ImageFX, or generating 300 variations in one day of our Best Phones Forever spots, using AI to generate copy and visuals. AI can help us do creative testing way faster, or respond to a brief with lots of ideas (or help us organise all the ideas we had but never shipped). Building a culture of experimentation on my team has always been a top priority. Now everyone, regardless of their role on the team, can make things and bring their ideas to life. And the most important part is that humans are in control. We are still the ones calling the shots and making sure that the final work we put out into the world meets our high bar. AI just helps us get there faster, bolder, and with more fun toys along the way. Exciting times! I really enjoyed chatting with Fast Company and Jeff Beer about how my team is harnessing #AI across every stage of the creative process, from ideation to creation. Check out our full conversation & let me know how you’re using AI in your creative process: https://lnkd.in/gxvv2UBD

Seeing is no longer believing. Are you ready for the era of synthetic reality? This entire scene? Not real. It’s AI-generated. Built with Seedance 2.0, we’ve reached the tipping point where synthetic video is virtually indistinguishable from reality. What changed wasn’t just the resolution—it was Temporal Consistency. The Death of the "Uncanny Valley" Earlier AI video struggled with the "glitch" factor: ❌ Flickering frames & unstable faces ❌ Broken physics & "liquid" limbs ❌ Inconsistent lighting & motion drift Today’s Diffusion + Transformer models have solved the coherence puzzle. Scenes are now cinematic, stable, and production-ready. The Shift is Massive We are moving from "interesting tech demos" to production-grade media pipelines. Market Growth: Generative AI is projected to surpass hundreds of billions this decade. Infrastructure: Massive investments are pouring into GPUs, NPUs, and hyperscale AI PCs. Volume: AI-generated content will soon represent the majority of online media. What’s Next? (The "Next Wave") 🎬 Real-time filmmaking: Describing a scene and seeing it rendered instantly. 👤 Digital Humans: Synthetic actors with perfect emotional range. 🎮 Interactive Worlds: Fully AI-created gaming environments. 🌍 Global Advertising: Personalized ads generated at scale in seconds. Content creation is pivoting. It’s becoming more about "describing ideas" than operating cameras or editing timelines. The Hard Truth As visuals become photorealistic, we enter an era where: Trust becomes harder to earn. Verification becomes a critical skill. Authenticity becomes the ultimate premium. The future of AI won't just reshape creativity—it will reshape human perception itself. #GenerativeAI #AI #Innovation via @deepnewsai #FutureOfWork #TechTrends #Seedance #AIReady #DigitalTransformation

Clicks are no longer the competition. Inclusion in AI-generated answers is. During a recent test with a generative search tool for a niche B2B service, the output was simple. A clean summary. A few companies mentioned. None of them were top-ranked in traditional search, and one had no visible paid presence. What stood out was not visibility, but clarity. This reflects a broader shift. AI now shapes discovery before a buyer ever reaches a website or speaks to sales. It interprets, filters, and presents brands based on how well their information is structured and understood. If that structure is weak, the brand does not just rank lower. It gets excluded. Publishing more content does not solve this. Structured clarity does. This week’s newsletter explores why semantic consistency, knowledge frameworks, and disciplined metadata are becoming real advantages. It also unpacks why volume without cohesion is starting to work against teams, not for them. For teams responsible for growth, brand, or go-to-market strategy, this shift is already in motion. The full piece dives deeper: AI Systems Mediate All Discovery.

Your job title doesn’t protect you anymore. Anyone can do 50% of your work now. AI doesn’t care if you’re a senior designer, art director, or a weekend Canva warrior. The gates of design are wide open. And 𝘦𝘷𝘦𝘳𝘺𝘰𝘯𝘦 just walked in. Your PM is mocking up UIs. Your intern’s feeding prompts into Midjourney. Even your client is showing you “inspo” they 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘦𝘥. And honestly? Some of it slaps. It’s a blow to the ego. But also a massive opportunity. Because AI isn’t killing design. It’s killing 𝘤𝘰𝘮𝘱𝘭𝘢𝘤𝘦𝘯𝘤𝘺. Here’s how to stay ahead: 1. Master prompt crafting.    This is your new brush, your new pen tool.    Don’t just ask AI for “a logo.”    Learn to 𝘥𝘪𝘳𝘦𝘤𝘵 it like a creative partner.    Use constraints, mood, style, and composition cues.    Vague prompts = vanilla results.     2. Focus on taste, not tools.    Your skill with Figma won’t save you    if your visual taste is mid.    AI levels the playing field on execution.    Taste, judgment, and aesthetic instinct    are now your competitive edge.     3. Become the editor, not the executor.    Anyone can generate 100 versions.    The designer of the future knows    which one 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘸𝘰𝘳𝘬𝘴, and why.    Your value lies in curation, not just creation.     4. Design systems > single screens.    AI can spit out mockups.    But can it build scalable, strategic design systems?    You still can.    Go beyond surfaces.    Think in ecosystems.     5. Marry data with design.    Learn to read the numbers.    Use AI to test, optimize, and adapt    designs in real-time.    Beautiful design is useless if it doesn’t convert. AI isn’t coming for your job. It’s coming for your 𝘰𝘭𝘥 𝘫𝘰𝘣 𝘥𝘦𝘴𝘤𝘳𝘪𝘱𝘵𝘪𝘰𝘯. Adapt. Or get designed out. ✌️ Curious to hear from you: What skill do you think will separate designers from the noise in the AI era? Drop it in the comments. Let’s talk.

He sold two tech companies before the age of 16. Today, he runs a $1.5B AI startup reshaping online shopping. John Imah is a Nigerian immigrant and self-taught builder whose story doesn’t follow the usual Silicon Valley script. He didn’t come through elite networks or polished pipelines. He learned by building early, obsessively, and repeatedly. As a teenager, he taught himself to code and went on to sell two technology companies before most people have chosen a university major. Instead of treating that as a finish line, he treated it as a foundation. He went on to work at Snapchat, Twitch, and Meta, where he deepened his understanding of platforms, user behavior, and what it actually takes to scale products used by millions. That mix of early entrepreneurship and big-tech exposure led him to a very specific insight: one of the biggest problems in online retail hasn’t been solved by branding, logistics, or marketing. It’s trust. More specifically, the inability for shoppers to truly know how something will fit them. That insight became SpreeAI. SpreeAI uses photorealistic AI try-on technology that allows shoppers to see clothing on their own bodies from a single photo, with up to 99% sizing accuracy. For consumers, it removes guesswork and frustration. For brands, it dramatically reduces returns, lowers costs, improves conversion rates, and tackles a major sustainability challenge in fashion. Backed by nearly $60M and valued at $1.5B in 2025, SpreeAI is a rare example of AI solving a real, global commerce problem at scale. John’s journey has been consistent. Early curiosity became experimentation then products and, eventually, companies. This is often what real leadership and innovation look like. Not chasing the next trend, but staying close to a problem long enough to understand it deeply and then building something that actually changes how people experience the world. What problem have you been circling for years, even as your tools, roles, and titles have evolved? Time to strike yet? #Leadership #Entrepreneurship #Nigeria #OurStories #ArtificialIntelligence #Retail #AfricanFounders #Storytelling Cc: The Numbers Game

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.