Innovation

This $850M startup is now shutting down and selling assets for just $116M. Humane Inc., founded by ex-Apple execs Imran Chaudhri and Bethany Bongiorno, aimed to revolutionize personal technology with its AI Pin - a wearable, voice-activated assistant. But now, less than a year after its launch, the AI Pin is gone, and Humane Inc. is being sold for spare parts. So, what went wrong? Let’s break it down: 1. Bold vision, flawed execution Humane marketed the AI Pin as an "iPhone killer," but failed at product design. Slow response times, overheating issues, and an awkward user experience made it feel like a prototype. 2. A flawed pricing strategy The $699 price was high as is, but the added $24/month subscription made possible customers say “I’ll just use my phone”. 3. Skipping real-world testing Poor battery life, laggy cloud processing, and unreliable voice commands made it impractical for everyday use - issues that should’ve been caught in testing. 4. Operating like a corporation, not a startup Humane followed Apple’s “big reveal” strategy instead of iterating based on user feedback. Prioritizing design over function, they ignored early warnings and launched an unfinished product. 5. No ecosystem, no adoption Unlike Apple or Google, the AI Pin had no app store, third-party integrations, or seamless device compatibility, leaving users with a standalone gadget that didn’t fit into their workflow. 6. Burned cash without a backup plan Despite raising $230M, Humane’s high burn rate meant they needed mass adoption fast. When early reviews highlighted flaws, demand collapsed, and they had no pivot strategy. - In my 25 years building healthtech products, I've learned that big-company experience doesn't always translate to startup success. Corporate executives often struggle with the rapid iteration and lean thinking startups need to survive. What do you think was Humane's biggest mistake? #innovation #ai #startups

AI Product Management AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications. This is making it possible to build new kinds of things, which in turn is driving shifts in best practices in product management — the discipline of defining what to build to serve users — because what is possible to build has shifted. In this post, I’ll share some best practices I have noticed. Use concrete examples to specify AI products. Starting with a concrete idea helps teams gain speed. If a product manager (PM) proposes to build “a chatbot to answer banking inquiries that relate to user accounts,” this is a vague specification that leaves much to the imagination. For instance, should the chatbot answer questions only about account balances or also about interest rates, processes for initiating a wire transfer, and so on? But if the PM writes out a number (say, between 10 and 50) of concrete examples of conversations they’d like a chatbot to execute, the scope of their proposal becomes much clearer. Just as a machine learning algorithm needs training examples to learn from, an AI product development team needs concrete examples of what we want an AI system to do. In other words, the data is your PRD (product requirements document)! In a similar vein, if someone requests “a vision system to detect pedestrians outside our store,” it’s hard for a developer to understand the boundary conditions. Is the system expected to work at night? What is the range of permissible camera angles? Is it expected to detect pedestrians who appear in the image even though they’re 100m away? But if the PM collects a handful of pictures and annotates them with the desired output, the meaning of “detect pedestrians” becomes concrete. An engineer can assess if the specification is technically feasible and if so, build toward it. Initially, the data might be obtained via a one-off, scrappy process, such as the PM walking around taking pictures and annotating them. Eventually, the data mix will shift to real-word data collected by a system running in production. Using examples (such as inputs and desired outputs) to specify a product has been helpful for many years, but the explosion of possible AI applications is creating a need for more product managers to learn this practice. Assess technical feasibility of LLM-based applications by prompting. When a PM scopes out a potential AI application, whether the application can actually be built — that is, its technical feasibility — is a key criterion in deciding what to do next. For many ideas for LLM-based applications, it’s increasingly possible for a PM, who might not be a software engineer, to try prompting — or write just small amounts of code — to get an initial sense of feasibility. [Reached length limit. Full text: https://lnkd.in/gYY-hvHh ]

Data Integration Revolution: ETL, ELT, Reverse ETL, and the AI Paradigm Shift In recents years, we've witnessed a seismic shift in how we handle data integration. Let's break down this evolution and explore where AI is taking us: 1. ETL: The Reliable Workhorse      Extract, Transform, Load - the backbone of data integration for decades. Why it's still relevant: • Critical for complex transformations and data cleansing • Essential for compliance (GDPR, CCPA) - scrubbing sensitive data pre-warehouse • Often the go-to for legacy system integration 2. ELT: The Cloud-Era Innovator Extract, Load, Transform - born from the cloud revolution. Key advantages: • Preserves data granularity - transform only what you need, when you need it • Leverages cheap cloud storage and powerful cloud compute • Enables agile analytics - transform data on-the-fly for various use cases Personal experience: Migrating a financial services data pipeline from ETL to ELT cut processing time by 60% and opened up new analytics possibilities. 3. Reverse ETL: The Insights Activator The missing link in many data strategies. Why it's game-changing: • Operationalizes data insights - pushes warehouse data to front-line tools • Enables data democracy - right data, right place, right time • Closes the analytics loop - from raw data to actionable intelligence Use case: E-commerce company using Reverse ETL to sync customer segments from their data warehouse directly to their marketing platforms, supercharging personalization. 4. AI: The Force Multiplier AI isn't just enhancing these processes; it's redefining them: • Automated data discovery and mapping • Intelligent data quality management and anomaly detection • Self-optimizing data pipelines • Predictive maintenance and capacity planning Emerging trend: AI-driven data fabric architectures that dynamically integrate and manage data across complex environments. The Pragmatic Approach: In reality, most organizations need a mix of these approaches. The key is knowing when to use each: • ETL for sensitive data and complex transformations • ELT for large-scale, cloud-based analytics • Reverse ETL for activating insights in operational systems AI should be seen as an enabler across all these processes, not a replacement. Looking Ahead: The future of data integration lies in seamless, AI-driven orchestration of these techniques, creating a unified data fabric that adapts to business needs in real-time. How are you balancing these approaches in your data stack? What challenges are you facing in adopting AI-driven data integration?

Should you try Google’s famous “20% time” experiment to encourage innovation? We tried this at Duolingo years ago. It didn’t work. It wasn’t enough time for people to start meaningful projects, and very few people took advantage of it because the framework was pretty vague. I knew there had to be other ways to drive innovation at the company. So, here are 3 other initiatives we’ve tried, what we’ve learned from each, and what we're going to try next. 💡 Innovation Awards: Annual recognition for those who move the needle with boundary-pushing projects. The upside: These awards make our commitment to innovation clear, and offer a well-deserved incentive to those who have done remarkable work. The downside: It’s given to individuals, but we want to incentivize team work. What’s more, it’s not necessarily a framework for coming up with the next big thing. 💻 Hackathon: This is a good framework, and lots of companies do it. Everyone (not just engineers) can take two days to collaborate on and present anything that excites them, as long as it advances our mission or addresses a key business need. The upside: Some of our biggest features grew out of hackathon projects, from the Duolingo English Test (born at our first hackathon in 2013) to our avatar builder. The downside: Other than the time/resource constraint, projects rarely align with our current priorities. The ones that take off hit the elusive combo of right time + a problem that no other team could tackle. 💥 Special Projects: Knowing that ideal equation, we started a new program for fostering innovation, playfully dubbed DARPA (Duolingo Advanced Research Project Agency). The idea: anyone can pitch an idea at any time. If they get consensus on it and if it’s not in the purview of another team, a cross-functional group is formed to bring the project to fruition. The most creative work tends to happen when a problem is not in the clear purview of a particular team; this program creates a path for bringing these kinds of interdisciplinary ideas to life. Our Duo and Lily mascot suits (featured often on our social accounts) came from this, as did our Duo plushie and the merch store. (And if this photo doesn't show why we needed to innovate for new suits, I don't know what will!) The biggest challenge: figuring out how to transition ownership of a successful project after the strike team’s work is done. 👀 What’s next? We’re working on a program that proactively identifies big picture, unassigned problems that we haven’t figured out yet and then incentivizes people to create proposals for solving them. How that will work is still to be determined, but we know there is a lot of fertile ground for it to take root. How does your company create an environment of creativity that encourages true innovation? I'm interested to hear what's worked for you, so please feel free to share in the comments! #duolingo #innovation #hackathon #creativity #bigideas

Powering Cities with Every Step: Japan’s Smart Energy Innovation ⚡🚶♂️ What if your daily walk could help power your city? In Japan, it already does. Train stations, sidewalks, and bridges are being fitted with piezoelectric sensors—materials that generate electricity from movement. 🔹 How It Works – Every footstep applies pressure, creating a tiny electric charge. Multiply that by thousands of daily commuters, and it’s enough to power LED screens, lights, and signage. 🔹 Real-World Impact – Tokyo train stations track how much energy passengers generate, turning commutes into a live science experiment. Bridges capture vibrations from cars to power streetlights. 🔹 The Big Picture – While this won’t replace traditional energy sources, it’s a step toward greener, self-sustaining infrastructure. 💡 Could this technology be scaled for more cities? Where else could we harvest untapped energy? Let’s discuss! 👇 #Innovation #SustainableEnergy #SmartCities #GreenTech #FutureInfrastructure

AI Product Management vs AI for Product Management: Hacks and resources for you. Regardless the path you're on, you need to evolve your PM Craft. 'Evolve' being the keyword here. 𝗙𝗼𝗿 𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 (This is for the PMs working directly with AI products) – think Research PMs, Recommendations PMs, Platform PMs, and so on. You really need to get good at handling AI's unique quirks: ✨ The Probabilistic nature of AI: It's not always 0 or 1, and you've got to navigate that uncertainty. ✨ The Deep dependency on good quality data: Garbage in, garbage out. You're constantly thinking about data quality. ✨ Developing deep AI awareness: This is key but it's not about you getting too deep into technical concepts you won't need. My secret hack is to make it a habit to read research blogs from big tech companies. Google AI, Meta AI, OpenAI and attending technical conferences. Here are some: -Google AI Blog: https://ai.google/ -DeepMind's blog https://lnkd.in/g3mi8Xxy -Meta AI Blog: https://ai.meta.com/blog/ -OpenAI Research Blog: https://lnkd.in/gR_kPSkt -Microsoft AI Blog: https://lnkd.in/gYkW63yz -Amazon Science Blog: https://lnkd.in/gMJzQrGG You'll literally see what's going to be the next big product in the next two years. The original Transformers paper came out in 2017 – a PM on top of their craft could have foreseen Generative AI tools coming years ago. 𝗙𝗼𝗿 𝗔𝗜 𝗳𝗼𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 ✨ This is about leveraging AI tools to have more impact as a PM, no matter what sector you're in. It's all about adjusting your work style and experimenting to see what actually works for you. My hack here is simple but effective: train your brain to try new things. I block my calendar for 2-hour "experimentation slots." During that time, I'm creating my own tutorials, trying out new AI tools on my actual work, and following the right people. You know most of the tools by now, here are some that you might want to check out: -NotebookLM: new features getting added very often -ChatPRD: https://www.chatprd.ai/ -Productboard AI: https://lnkd.in/gm2mfeDY -ProdPad CoPilot: https://lnkd.in/gWrZZd7W -Quantilope: https://lnkd.in/g3TUJ_-9 -Dovetail: https://dovetail.com/ -Notion AI: https://lnkd.in/gfUb8yKg -Mixpanel: https://mixpanel.com/ Regardless of your seniority, being hands-on and experimenting with these tools goes a long way.

*𝑆𝑖𝑔ℎ* Yet again, I hear another company excitedly talking about implementing AI—integrating it, scaling it, “revolutionizing everything”—and yet they gloss over the need for a robust data strategy. It takes all my energy not to pull my hair out as I cringe, listening to the words. But instead of yelling into the void, I’ve learned a better approach: I ask questions. Good ones. The kind that make leaders pause and realize that AI without solid data foundations is just a very expensive experiment. 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐥𝐢𝐤𝐞: 1) What percentage of your data is truly usable—normalized, contextualized, indexed, and properly mapped? 2) How much of your data is “dark” (produced but unused), and what’s your plan to leverage it? 3) Do you have a defined data governance and data management framework, or is it mostly ad hoc? 4) What’s your process for ensuring data accuracy, completeness, and relevance for AI models? 5) How scalable is your data infrastructure to support AI at an enterprise level? 6) If AI solutions depend on a continuous flow of clean data, how confident are you that your processes can deliver that over time? This is when the lightbulb flickers. Because here’s the reality: You already produce more data than you know what to do with. And yet, no one is asking whether your data is reliable, clean, and strategically aligned. Oh, and let’s not forget—you’re probably not even collecting the right strategic data yet to unlock AI’s full potential. AI doesn’t live in isolation. It thrives on organized, high-quality data. Your first step to scaling AI shouldn’t be building models—it should be building a foundation: ✅ 𝐃𝐚𝐭𝐚 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 ✅ 𝐃𝐚𝐭𝐚 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 ✅ 𝐃𝐚𝐭𝐚 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 ✅ And, most importantly, a 𝐝𝐚𝐭𝐚 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲. 𝐒𝐨 𝐛𝐞𝐟𝐨𝐫𝐞 𝐲𝐨𝐮 𝐝𝐢𝐯𝐞 𝐢𝐧𝐭𝐨 𝐀𝐈, 𝐚𝐬𝐤 𝐲𝐨𝐮𝐫𝐬𝐞𝐥𝐟: “If AI is the engine of innovation, do we even have the fuel to power it?” (Trust me, the answer might surprise you.) ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Innovation doesn’t always mean building something new. Sometimes, the most impactful breakthrough comes from using existing products in new ways. Our Bill & Melinda Gates Foundation partners at the Centre de Recherche et d’Innovations en Sante Publique (CRISP), the Niger Ministry of Health, and University of California, San Francisco are looking at how the common antibiotic azithromycin can reduce child mortality rates in Niger and other African countries. These studies have confirmed giving azithromycin to kids twice a year can prevent common infections and reduce child mortality by up to 14 percent. Those are mind blowing results when you think about how cheap, safe, and scalable the drug is.

How can leaders transform their teams to be AI-first? It starts with mindset. An AI-first mindset means: Seeing AI as an opportunity, not a threat. Viewing AI as a tool to augment teams, not just automate tasks. Using AI to reimagine work, not just optimize work. As leaders, it’s on us to build this mindset within our teams. Here are 5 ways we do this at HubSpot: Use AI daily: Lead by example—trust grows when teams see leaders embrace AI themselves. I use it everyday and share very specific use cases with our company on how I use it. Now every leader is doing the same with their teams. The result is that we will have almost everyone in the company use AI daily by the end of year. Apply constraints: Give clear, focused challenges. We kept headcount flat in Support while growing the customer base by 20%+. Result - the team innovated with AI and over achieved the target. Smart constraints drive innovation. Establish tiger teams: Empower small, agile groups to experiment, innovate, and teach the organization. We have AI Tiger teams in every function - they share progress in Slack channels and there is so much energy with small groups experimenting and learning. Be a learn-it-all: Foster a culture of continuous learning. Share openly about successes and failures alike. We have dedicated 2 full days to learning and scaling with AI this quarter as a company - we have lined up great speakers, ways to experiment and gamified learning. Measure progress and share it: Measure which teams are completing learning modules, using AI everyday and share that openly. A little healthy competition goes a long way in driving AI-fluency. AI isn’t just a technology shift. It’s fundamentally reshaping how work gets done—and that requires shifting our mindset first. Leaders who embrace AI now will unlock creativity, performance, and impact. Are you building an AI-first mindset with your team? #Leadership #AI #Innovation #Mindset #FutureOfWork

A ceramics teacher split her class into two groups. To one group, she gave a simple instruction: “Make as many pots as you can. You’ll be graded on quantity.” To the other, she said: “Make just one pot. But make it perfect. You’ll be graded on quality.” At the end of the week, something unexpected happened. The quantity group, in their rush to produce, iterated constantly. They tried new shapes. They learned from cracked handles and warped lids. Each failure taught them something. By the end, they had made not only the most pots — but also the best ones. The quality group? They spent most of their time theorizing. Planning. Sketching. Worrying. Their pot, though done, was far from perfect. This story plays out in plenty of situations and initiatives, big or small. When building products, don’t wait for perfection. Start building. Ship. Learn. Repeat. Iteration is how excellence emerges. Doing is how you find direction. Especially in today’s AI-fueled world, where the ground is shifting fast — the teams that move, learn, and ship quickly will beat the ones that wait for perfect specs. So the next time you find yourself overthinking, remember the pottery class. Just make another pot.