Michael Otte’s Post

We love summer in the garden too, but before we get carried away with new models, let's take a step back and look at the challenges that need to be solved for companies to drive real success with AI initiatives. Here are the two big challenges we're seeing: 1. Limited In-House Expertise: Without the right skills, AI development can drag and miss key opportunities as best practices keep changing. 2. Lack of Infrastructure: Without solid tools and infrastructure, testing and deploying AI models can become a costly guessing game. At Vellum, we're addressing these challenges by offering the infrastructure and hands-on support that product and engineering teams need to build reliable AI systems. We're doing this daily, working alongside an impressive group of teams to build, test, and deploy their AI applications. We've spotted some common threads, and in our latest article, we're breaking down how we tackle these challenges and the steps you need to take to build trust in your AI systems. In our latest report, we've outlined 6 critical stages your product and eng teams need to nail down to identify high-impact AI opportunities, validate them, and then deploy and monitor in production with confidence. Check out the report in comments, and let me know what you think!

🌐 Unlocking the YEAR 2025 with Development: AI Agents in Action 🌐 By the end of this month, we’re going to start seeing the true benefits of AI agents, digital twins, and deepfake technology. These advancements may sound futuristic, but they're already transforming how we work and connect, and we’re just at the beginning. If there’s one thing that stands out, it’s the power of the AI agent. Over the past years, we’ve heard terms like ChatGPT, large language models, and more. But as we approach 2025, AI agents are set to become the driving force behind development. And here’s why: they aren’t just tools – they’re partners in scaling innovation. Take a recent AWS report, for instance: by leveraging AI-generated code, they saved an impressive 2,000 developer years! That’s a massive performance milestone, showing that AI isn’t just improving efficiency but redefining it. In the coming weeks, I’ll dive into how AI can support and expand your coding capabilities, ultimately enhancing productivity. 🔗 Join us for our upcoming KISS (Keep it Interval, Scalable, and Sustainable) webinar, where we’ll explore these insights further and answer your questions. This use case will surely pique your interest! https://lnkd.in/gstQHcPE See you there!

I've been exploring how various startups are applying generative AI, and I'm genuinely amazed—not just by the innovative use cases but by the tangible value their customers are experiencing. These range from software development and maintaining outdated legacy code (often written in languages no one wants to touch) to being first line of autonomous support for applications and infrastructure, and even being your SecOps. I've started incorporating generative AI into my daily work, and the efficiency gains are beyond description. Tasks like creating strategy documents, pitch decks, and requirement docs that used to take weeks now only take a matter of hours. For those skeptical of AI, I urge you to start using it—the value becomes evident almost immediately. I can’t even begin to imagine how transformative the world will be five years from now. #AI #GenAI #EconomicValue

Is consumer facing AI still a promise? Let me come clean, I truly believed that the Humane pin and Rabbit R1 would revolutionize the product design landscape. The user would be freed from the shackles of interacting with endless interfaces, now only having to give a simple command. I should have know better. After watching numerous disastrous videos on YouTube, I can only conclude one thing: We aren't there yet, and it doesn't seem like we'll be there anytime soon. Getting LLM right is just incredibly challenging. So what comes next? Do we have to wait until the next startup succeeds after several have failed? For now, it looks like big tech is going to pick up the tab. They have the money, energy, and platform dominance to push their policies forward. However, to make AI successful as a consumer-facing product, big tech needs to overcome two major challenges within: a system that delivers truly helpful results by understanding the full context, and communicates as naturally as talking to a person. As of now, we have seen glimpses of what may come. For the time being, I entertain myself with less potent forms of artificial intelligence, such as Siri, which, after 13 years, promises to finally help with your tasks instead of just providing references to websites. Let's see. #productdesign #AI

Good guidance from an expert when you are thinking about incorporating AI tech into your startup ⬇️

Exciting times in the AI world! Convergence has just secured $12 million in pre-seed funding to launch Proxy, a revolutionary personalized AI that's set to redefine our relationship with technology. Founded by former Shopify and Cohere experts, Proxy learns and adapts over time, tackling repetitive tasks and evolving like a human. Imagine an AI that schedules meetings, orders groceries, and continually learns from your interactions—freeing up your time for what truly matters! With backing from industry giants like Balderton Capital and Salesforce Ventures, Proxy is positioned to disrupt how we integrate AI into our daily lives. As demand for smarter tools rises, can we expect to see AI that not only assists but grows alongside us? I believe Proxy's innovative approach will shape the future of productivity across industries. Let's embrace this transformative potential! What's your take on the evolving role of AI in our lives?

Regarding the current AI vendor landscape: We need more who can clearly articulate the problem their product/solution helps to solve and how actual value can be achieved. We need less who focus their pitch/discussions around theoretical concepts, research papers, and rants about the path to AGI. While I understand the value in both (research drives innovation, innovation drives new value streams), businesses who want to actually implement AI solutions want to buy impact/value, not theory. If you cannot clearly articulate your product/solution in this context, you will likely be bucketed into "just another AI startup".

With over 4000 AI startups in the US alone, your chances of picking the right AI tooling and not having to swap out is near zero. Reach out if you'd like to learn how Data Integration as Code makes it easy to swap out different AI tools as the market evolves. Composability drives agility, agility drives success in uncertain business conditions. #AI #Composability #DataIntegrationAsCode #DataIntegration #DataOps

Unfortunate (or maybe purposeful) brand name aside, Bland AI is on track to entirely change the customer service space. A $22m Series A investment shows this thesis isn't mine alone. Bland can manage thousands of calls at once. Outbound, and inbound. And because it's got AI inside, it's constantly improving. ToS ( https://lnkd.in/drtw5MvU ) are a little discomforting to the naive eye (mine), but as Bland becomes embedded in the operations of more businesses, learning at scale its opportunities and limitations, you have to imagine that things will become clearer in the months, and years, to come. Bullish about Bland. AI is finally coming of age for the enterprise. Partner Bland with Glean - and you have something spectacular in the wings for any organisation that's invested in innovation to grow. Let's see what customers think... https://www.bland.ai/

Very interesting playbook for #GenerativeAI in the enterprise. There's a ton in here to digest, but here's my biggest takeaway, so far: it's not about automating tasks, it's about revolutionizing workflows. The future belongs to those who can rebundle work intelligently while working to create sustainable, responsible AI ecosystems. #EnterpriseAI #AiStrategy #FutureOfWork #AiEthics