Innovation Challenges in Tech

Most people will hear an idea and say, “Wow, what a great pitch!” just to be polite. Others might have no clue what they’re talking about and still tell you why you’ll fail. Both of these opinions? Noise. I’ve spent my career searching for ideas that have the potential to transform industries. Those ideas are rarely obvious, but one of the signs you might be on to something big is thoughtful pushback from people who truly understand the industry you’re working in. That’s what makes an idea contrarian. It’s not about swimming against the tide for the sake of being different. It’s about challenging conventional wisdom with a well-reasoned theory of the game you’re playing, and why your idea will win. The key is balancing confidence in your vision with humility—being willing to listen, learn, and refine your idea based on feedback from those same smart skeptics. Their pushback isn’t just a roadblock; it’s a signal. And if you can navigate that tension, you might just have something extraordinary on your hands.

Exciting updates on Project GR00T! We discover a systematic way to scale up robot data, tackling the most painful pain point in robotics. The idea is simple: human collects demonstration on a real robot, and we multiply that data 1000x or more in simulation. Let’s break it down: 1. We use Apple Vision Pro (yes!!) to give the human operator first person control of the humanoid. Vision Pro parses human hand pose and retargets the motion to the robot hand, all in real time. From the human’s point of view, they are immersed in another body like the Avatar. Teleoperation is slow and time-consuming, but we can afford to collect a small amount of data.  2. We use RoboCasa, a generative simulation framework, to multiply the demonstration data by varying the visual appearance and layout of the environment. In Jensen’s keynote video below, the humanoid is now placing the cup in hundreds of kitchens with a huge diversity of textures, furniture, and object placement. We only have 1 physical kitchen at the GEAR Lab in NVIDIA HQ, but we can conjure up infinite ones in simulation. 3. Finally, we apply MimicGen, a technique to multiply the above data even more by varying the *motion* of the robot. MimicGen generates vast number of new action trajectories based on the original human data, and filters out failed ones (e.g. those that drop the cup) to form a much larger dataset. To sum up, given 1 human trajectory with Vision Pro  -> RoboCasa produces N (varying visuals)  -> MimicGen further augments to NxM (varying motions). This is the way to trade compute for expensive human data by GPU-accelerated simulation. A while ago, I mentioned that teleoperation is fundamentally not scalable, because we are always limited by 24 hrs/robot/day in the world of atoms. Our new GR00T synthetic data pipeline breaks this barrier in the world of bits. Scaling has been so much fun for LLMs, and it's finally our turn to have fun in robotics! We are creating tools to enable everyone in the ecosystem to scale up with us: - RoboCasa: our generative simulation framework (Yuke Zhu). It's fully open-source! Here you go: http://robocasa.ai - MimicGen: our generative action framework (Ajay Mandlekar). The code is open-source for robot arms, but we will have another version for humanoid and 5-finger hands: https://lnkd.in/gsRArQXy - We are building a state-of-the-art Apple Vision Pro -> humanoid robot "Avatar" stack. Xiaolong Wang group’s open-source libraries laid the foundation: https://lnkd.in/gUYye7yt - Watch Jensen's keynote yesterday. He cannot hide his excitement about Project GR00T and robot foundation models! https://lnkd.in/g3hZteCG Finally, GEAR lab is hiring! We want the best roboticists in the world to join us on this moon-landing mission to solve physical AGI: https://lnkd.in/gTancpNK

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

New product initiatives within large companies often fail to achieve their potential because they have too much rather than too little. They have too much: 1) Headcount You are now under pressure to come up with something for all these people to do. Especially in cultures where “engineers must always be coding” and a PM is seen as failing if engineers are even briefly “blocked on requirements.” 2) Democratic decision making Creative ideas get killed (or watered down) by groups — yet this is the default in most big companies, even those that claim to use RAPID or similar frameworks. 3) Optics requirements You must now manufacture metrics and milestones to show straight-line progress and demonstrate certainty — during what is, by its very nature, an uncertain journey. 4) Involvement of the “core” product group To appease the leaders of the company’s cash cow, you make compromises that weaken your product. These leaders have the most power within the company and some may even try to confuse the CEO or quietly sabotage your initiative. 5) Reliance on the company’s distribution Due to the mirage of distribution, you won’t be incentivized to deeply understand your customer like a real startup would. Your initial traction is misleading — you get a usage spike, but: (a) those users are scattered across segments, not your core segment (have you even identified that core segment?) (b) what’s given will be taken away — that homepage slot for your new product will disappear next quarter due to VP jealousy or shifting OKRs (with some hand-wavy “metrics neutral” excuse). So if you are leading a new initiative within a larger company and your CEO/CxO asks you what you need to succeed, do not default to the answer that everyone in this situation gives: “I need more resources”. Instead, consider asking for less — less reporting, less certainty, less consensus-driven decision making, less meddling, and less pressure to build out a “full team” & great operations early on. If your CEO is competent, they’ll respect it. (clearly, this entire post is only for the intrepid product leaders who want to make winning products, it is not for everyone 🙂)

Strategy is all about anticipating and creating a desired future. To prepare for this, it is essential to understand the Futures Cone, outlining five types of future. There is no such thing as “the future.” It all depends on what we mean, how far we look ahead and on whether we are trying to predict or create the future. One of the most helpful tools to understand this is Hancock and Bezolt’s (1994) “Futures Cone.” It describes five different types of future. They are PROJECTED FUTURE The future we tend to get when we simply stick to business as usual and extrapolate the current baseline strategy. It’s more of the same and contains the least uncertainty. PROBABLE FUTURE The future that most likely is going to happen, taking into account trends and developments within and outside the organization. It’s a bit more uncertain, but still quite predictable. PLAUSIBLE FUTURE The future that could happen according to our current knowledge. This is broader than just the probable future and includes futures that we could foresee rather than just expect. POSSIBLE FUTURE The broadest type of future, including everything that might happen. This is the realm of our imagination and extends beyond our current knowledge, tools and technologies. PREFERABLE FUTURE The future that we want to happen. This is different from the four above as it reflects our desires, preferences and intentions rather than what we cognitively can anticipate. As the image illustrates, the Preferable Future often deviates from the Projected Future (business as usual) or Probable Future (following the trends). This means it requires active imagination and bringing in our desires and intentions to imagine a future that is different. At the same time, it also shows that the Preferable Future should mostly reside within the boundaries of the Plausible Future with perhaps a touch of the Possible Future. Otherwise the gap between where you are today and how you want your future to look is too big. This is where the distinction is made between organizations that make smaller, incremental changes, and those that create breakthrough innovations. The further you can stretch your Preferred Future away from the Projected Future towards the Plausible and Possible Futures, the more visionary you need to be, and the more you will be an industry leader. Here’s the question for you: where is your Preferred Future targeted—more of the same (Projected or Probable) or at creating something new (Plausible and Possible)? — For more useful strategy and leadership content, join my Soulful Strategy newsletter: https://lnkd.in/eKjb8Uss #forecast #futureinsight #impactleaders

The Co-Founder Dynamic: How Varun Alagh and I Navigate Disagreements "Show me your numbers." That's become our default response whenever we disagree. Not "you're wrong" or "trust me on this", just "show me your numbers." This approach was born from a heated 2017 argument in our living room, in front of our son, over a product launch decision. Varun wanted to delay, I wanted to ship. We were both passionate, both convinced we were right. But we were both arguing from gut feelings, not facts. Now, years later, here's how we handle disagreements: 1. Data Wins, Egos Lose When we disagree, we each gather our strongest data points within 24 hours. Market research, consumer feedback, financial projections, competitor analysis: whatever supports our position. Then we compare. The stronger data set wins. 2. Define Decision-Making Domains We divided responsibilities clearly to minimize overlap conflicts. And while some decisions we still take together, the overall result is 80% fewer conflicts because we know who has the final say. 3. The 24-Hour Rule for Major Disagreements If the data is inconclusive or we can't agree after reviewing the numbers, we sleep on it. Emotions cool down, egos step aside, and new perspectives often emerge. Our best decisions come from our second conversation, not our first argument. The deeper truth: Our different perspectives make us stronger. Varun's analytical approach balances my intuitive decisions. My market instincts complement his operational rigor. But data grounds both of us. What we've learned: • Two founders agreeing all the time means one is unnecessary • Healthy conflict leads to better decisions—if it's fact-based • Respect for data matters more than being right • The best arguments are won with evidence, not emotion #CoFounderDynamics #Entrepreneurship #StartupLessons

It’s easy as a PM to only focus on the upside. But you'll notice: more experienced PMs actually spend more time on the downside. The reason is simple: the more time you’ve spent in Product Management, the more times you’ve been burned. The team releases “the” feature that was supposed to change everything for the product - and everything remains the same. When you reach this stage, product management becomes less about figuring out what new feature could deliver great value, and more about de-risking the choices you have made to deliver the needed impact. -- To do this systematically, I recommend considering Marty Cagan's classical 4 Risks. 𝟭. 𝗩𝗮𝗹𝘂𝗲 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗦𝗼𝘂𝗹 𝗼𝗳 𝘁𝗵𝗲 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 Remember Juicero? They built a $400 Wi-Fi-enabled juicer, only to discover that their value proposition wasn’t compelling. Customers could just as easily squeeze the juice packs with their hands. A hard lesson in value risk. Value Risk asks whether customers care enough to open their wallets or devote their time. It’s the soul of your product. If you can’t be match how much they value their money or time, you’re toast. 𝟮. 𝗨𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗨𝘀𝗲𝗿’𝘀 𝗟𝗲𝗻𝘀 Usability Risk isn't about if customers find value; it's about whether they can even get to that value. Can they navigate your product without wanting to throw their device out the window? Google Glass failed not because of value but usability. People didn’t want to wear something perceived as geeky, or that invaded privacy. Google Glass was a usability nightmare that never got its day in the sun. 𝟯. 𝗙𝗲𝗮𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗔𝗿𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗣𝗼𝘀𝘀𝗶𝗯𝗹𝗲 Feasibility Risk takes a different angle. It's not about the market or the user; it's about you. Can you and your team actually build what you’ve dreamed up? Theranos promised the moon but couldn't deliver. It claimed its technology could run extensive tests with a single drop of blood. The reality? It was scientifically impossible with their tech. They ignored feasibility risk and paid the price. 𝟰. 𝗩𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗠𝘂𝗹𝘁𝗶-𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹 𝗖𝗵𝗲𝘀𝘀 𝗚𝗮𝗺𝗲 (Business) Viability Risk is the "grandmaster" of risks. It asks: Does this product make sense within the broader context of your business? Take Kodak for example. They actually invented the digital camera but failed to adapt their business model to this disruptive technology. They held back due to fear it would cannibalize their film business. -- This systematic approach is the best way I have found to help de-risk big launches. How do you like to de-risk?

Technical debt isn’t just an IT problem—it’s an enterprise-wide drag on transformation and evolution ⛔. And a show-stopper for AI multi-agent systems. Left unchecked, it erodes business agility, locks innovation behind constraints, and amplifies risk across architectures. But technical debt is more than one thing, it plays out across all the four architecture domains: Business, Application, Data, and Technology Architectures: 🔹 Business Debt: Misaligned capabilities, redundant processes, and legacy constraints slow down strategic execution. Scaling AI, automation, or new business models? Good luck if you’re trapped in outdated operating models. 🔹 Application Debt: Spaghetti integrations, monolithic structures, and brittle workflows create friction for change. Every new initiative turns into a costly workaround instead of an accelerant. 🔹 Data Architecture: Inconsistent, duplicated, and poorly governed data corrupts decision intelligence. AI and analytics investments won’t drive value if they rely on unreliable, siloed, or inaccessible data. 🔹 Technology Architecture: Legacy infrastructure, technical sprawl, and fragmented ecosystems increase operational risk and limit scalability. The shift to cloud, AI, and modern platforms gets bogged down by outdated dependencies. 💡 Transformation isn’t just about adopting new technology—it’s about managing and eliminating technical debt. 🔹 Tackle it proactively with architectural guardrails, modernisation roadmaps, and incremental refactoring. 🔹 Quantify the cost—how much is technical debt limiting business innovation, AI adoption, or operational resilience? 🔹 Embed technical debt management into governance frameworks to ensure it doesn’t accumulate unchecked. 🚀 Organisations that treat technical debt as a strategic risk—not just an IT burden—will be the ones that evolve faster, innovate smarter, and scale sustainably. How does your organisation approach technical debt? Let’s discuss. 👇 #EnterpriseArchitecture #TechnicalDebt #AI #BusinessArchitecture #ApplicationArchitecture #DataArchitecture

Innovation is only as valuable as the problem it solves. We live in an age where technological advancements move faster than our ability to strategically adopt them. It’s no longer a question of can we implement this? but rather, should we? The real challenge isn’t access to innovation. 𝐈𝐭’𝐬 𝐝𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐞. Discipline to pause before we purchase. Discipline to align tools with outcomes. Discipline to measure impact before we declare success. 𝐓𝐡𝐞 𝐃𝐫𝐢𝐯𝐞𝐫𝐬 𝐨𝐟 𝐭𝐡𝐞 𝐓𝐞𝐜𝐡 𝐏𝐚𝐫𝐚𝐝𝐨𝐱: • 𝐒𝐡𝐢𝐧𝐲 𝐍𝐞𝐰 𝐎𝐛𝐣𝐞𝐜𝐭 𝐒𝐲𝐧𝐝𝐫𝐨𝐦𝐞: The irresistible pull towards the ‘new’ and ‘novel’, often at the expense of sustained objectives and an overarching strategic vision. • 𝐅𝐞𝐚𝐫 𝐨𝐟 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐎𝐮𝐭 (𝐅𝐎𝐌𝐎): The anxiety that failing to adopt new technologies or trends could result in missed opportunities for growth or competitive advantage. 𝐓𝐡𝐞 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 𝐂𝐡𝐞𝐜𝐤: • 𝟑𝟎% of App deployments fail • 𝟕𝟎% of Digital Transformation initiatives don’t meet goals • 𝟕𝟎%+ of manufacturers worldwide are stuck in pilot purgatory • 𝟓𝟖% of IoT projects are considered not to be successful • 𝟔𝟏% of manufacturers don’t have specific metrics to measure the effectiveness or impact of AI deployments 𝐀𝐝𝐯𝐢𝐜𝐞 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐓𝐞𝐜𝐡-𝐂𝐮𝐫𝐢𝐨𝐮𝐬 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬: 1. 𝐀𝐬𝐬𝐞𝐬𝐬, 𝐃𝐨𝐧'𝐭 𝐀𝐬𝐬𝐮𝐦𝐞: Evaluate whether the technology fills a need or optimizes current operations before investing. 2. 𝐀𝐥𝐢𝐠𝐧, 𝐓𝐡𝐞𝐧 𝐀𝐜𝐭: Ensure that any new tech acquisition is in alignment with your strategic business goals. 3. 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐭𝐨 𝐌𝐚𝐧𝐚𝐠𝐞: Develop clear metrics or KPIs to track the success and relevance of your technology investments. 𝐅𝐨𝐫 𝐚 𝐝𝐞𝐞𝐩𝐞𝐫 𝐝𝐢𝐯𝐞 𝐨𝐧 𝐭𝐡𝐢𝐬 𝐭𝐨𝐩𝐢𝐜, 𝐢𝐧𝐜𝐥𝐮𝐝𝐢𝐧𝐠 𝐬𝐨𝐮𝐫𝐜𝐞𝐬:  https://lnkd.in/eX89kQ6n ******************************************* • 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!

One of the first mistakes I made when launching my first regulated business was delegating compliance. I started with TransferTo, a mobile micro value transfer service, which wasn’t regulated. Eventually, TransferTo split into two branches (now DT One and Thunes), with the new branch handling actual money transfers that required regulatory compliance. At that time, I thought, "I'll hire a Chief Compliance Officer and let them set up the function," just as I did with marketing or tech. That was a mistake. I faced significant challenges in opening a bank account because I hadn't fully mastered my processes. I also had a hard time communicating with my compliance officer. I didn't have the words or the right codes. Regulatory compliance is ultimately the responsibility of the company and its leadership—it cannot be outsourced. As a CEO, I believe it's essential to make the effort to understand it because the risks for the company are too significant. The least severe risk is a fine. The moderate risk is a suspension of the license. The most severe risk is revocation, or even imprisonment. To effectively manage these risks, I believe it's the CEO's duty to establish the compliance framework. Get your hands dirty. Understand the mechanics. Then, the Chief Compliance Officer can execute your plan. And this is exactly what regulators expect. The CEO's ability to manage compliance is one of the key aspects they evaluate when you apply for a licence. They don't require you to know how to code, but they do expect you to fully understand your company's compliance. If I have one piece of advice for a fintech entrepreneur: invest in compliance. The stakes are too high. As a startup, it could destroy your business. As a scale-up, it could strongly hinder your growth.