Engineering

AI raised the floor. Engineering excellence raises the ceiling. It's so riveting to see new LLM models get published and the step changes that are happening. AI has made it dramatically easier to produce code. It has simultaneously made it much harder to hide weak engineering fundamentals. AI is raising the floor, meaning more people can generate software and prototypes quickly. But engineering excellence raises the ceiling: determining whether that code becomes a reliable, scalable system that actually creates enterprise value. AI is exposing something many organizations have quietly carried for years: technical debt, fragile architectures, and disconnected data foundations. When systems aren't built well, AI doesn't fix that. It simply reveals it faster. 💡  𝗜 𝗮𝗺 𝗮 𝘀𝘁𝗿𝗼𝗻𝗴 𝗯𝗲𝗹𝗶𝗲𝘃𝗲𝗿 𝘁𝗵𝗮𝘁 𝘁𝗼 𝗺𝗮𝘅𝗶𝗺𝗶𝘇𝗲 𝗔𝗜 𝘃𝗮𝗹𝘂𝗲, 𝘄𝗲 𝗻𝗲𝗲𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗲𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲. So what does engineering excellence look like right now? I think about it as four pillars: ▸ 𝗔𝗜-𝗥𝗲𝗮𝗱𝘆 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: AI doesn't work well on top of poor architecture. Modernizing legacy code without addressing underlying structure just produces the wrong architecture faster. ▸ 𝗛𝗶𝗴𝗵-𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗗𝗮𝘁𝗮 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀: AI is only as intelligent as the data it reasons over. You can't shortcut this layer and even a strong foundation must continuously evolve. ▸ 𝗦𝗲𝗰𝘂𝗿𝗲 𝗮𝗻𝗱 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗹𝗲 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: As AI agents become more autonomous, seeing what's happening and why becomes non-negotiable. Governance isn't just policy it's instrumentation and operationalization, as many of you noted in my last post. ▸ 𝗗𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀: Spec discipline, test rigor, strong code review, clear ownership are not legacy practices to abandon, but more important than ever. AI rewards good fundamentals and makes the consequences of weak ones more visible, faster. There's a real shift in how engineers spend their time. Less writing foundational code. More orchestrating systems: designing architecture, shaping how AI agents interact, validating outputs with genuine judgment. I see our senior engineers flying because their systems thinking depth makes AI a true force multiplier. Earlier-career engineers are learning, but need more deliberate mentorship than ever. When AI can simulate senior output, the risk is gaining confidence without gaining understanding. The best thing leaders can do: create conditions where engineers are proud of how they build, not just what they ship. The time savings alone aren't the win. For us, we are investing in deeper architecture work, stronger data foundations, the next generation of agentic capabilities and I believe that's the winning combo. 𝗗𝗼 𝘆𝗼𝘂 𝗮𝗴𝗿𝗲𝗲 𝘁𝗵𝗮𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗲𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲 𝗶𝘀 𝗺𝗼𝗿𝗲 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝘁𝗵𝗮𝗻 𝗲𝘃𝗲𝗿?

Scaling from 50 to 100 employees almost killed our company. Until we discovered a simple org structure that unlocked $100M+ in annual revenue. In my 10+ years of experience as a founder, one of the biggest challenges I faced in scaling was bridging the organizational gap between startup and enterprise. We hit that wall at around 100~ employees. What worked beautifully with a small team suddenly became our biggest obstacle to growth. The problem was our functional org structure: Engineers reporting to engineering, product to product, business to business. This created a complex dependency web: • Planning took weeks • No clear ownership  • Business threw Jira tickets over the fence and prayed for them to get completed • Engineers didn’t understand priorities and worked on problems that didn’t align with customer needs That was when I studied Amazon's Single-Threaded Owner (STO) model, in which dedicated GMs run independent business units with their own cross-functional teams and manage P&L It looked great for Amazon's scale but felt impossible for growing companies like ours. These 2 critical barriers made it impractical for our scale: 1. Engineering Squad Requirements: True STO demands complete engineering teams (including managers) reporting to a single owner. At our size, we couldn't justify full engineering squads for each business unit. To make it work, we would have to quadruple our engineering headcount. 2. P&L Owner Complexity: STO leaders need unicorn-level skills: deep business acumen and P&L management experience. Not only are these leaders rare and expensive, but requiring all these skills in one person would have limited our talent pool and slowed our ability to launch new initiatives. What we needed was a model that captured STO's focus and accountability but worked for our size and growth needs. That's when we created Mission-Aligned Teams (MATs), a hybrid model that changed our execution (for good) Key principles: • Each team owns a specific mission (e.g., improving customer service, optimizing payment flow) • Teams are cross-functional and self-sufficient,  • Leaders can be anyone (engineer, PM, marketer) who's good at execution • People still report functionally for career development • Leaders focus on execution, not people management The results exceeded our highest expectations: New MAT leads launched new products, each generating $5-10M in revenue within a year with under 10 person teams. Planning became streamlined. Ownership became clear. But it's NOT for everyone (like STO wasn’t for us) If you're under 50 people, the overhead probably isn't worth it. If you're Amazon-scale, pure STO might be better. MAT works best in the messy middle: when you're too big for everyone to be in one room but too small for a full enterprise structure. image courtesy of Manu Cornet ------ If you liked this, follow me Henry Shi as I share insights from my journey of building and scaling a  $1B/year business.

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

This is probably the most valuable tip I share with students and clients who want to get ahead in their professional lives: → Track your wins!! In a document (Excel, Word, or whatever works for you), create three columns: 1. TASK – What was it? ↳ Led a team meeting to resolve a bottleneck in the project timeline. 2. ACTION – What did you actually do? ↳ Facilitated a structured discussion to identify roadblocks, proposed a revised workflow, and reassigned tasks based on individual strengths and deadlines. 3. IMPACT – What measurable difference did it make? ↳ Reduced project timeline by 15%, increased task completion rate by 20%, and improved overall team alignment and morale. Update it at the end of each week. It’s such a simple approach, but it ensures you’re always ready to showcase your value when it matters most - whether it’s for performance reviews, job interviews, or pitching yourself for your next big opportunity. Highly recommend it! P.S. Have you ever tried something like this to keep track of your achievements? #careergrowth

#Diversity in high-tech fields remains critically low. The Equal Employment Opportunity Commission (EEOC) recently reported that #Black and #Latino professionals are underrepresented in high-tech roles, especially in leadership. These numbers highlight ongoing structural barriers in hiring, promotion and retention. This gap is a missed opportunity to tap into a wealth of diverse talent and perspectives essential to the future of tech. However, addressing and thoroughly fixing these challenges will require time, consistent effort and a long-term commitment to systemic change. Companies can support the progression of representation in tech by investing in training, mentorship and internship opportunities that open doors for people who were historically shut out. Programs like internXL, a platform that is committed to increasing diversity and inclusion in the internship hiring process for top companies, are making a significant impact. Similarly, the expansion of STEM education at institutions like Cornell University is helping to connect talented young people from underrepresented communities with opportunities for high-tech careers. When we work together to remove these barriers, we’re fostering a more inclusive workforce and strengthening innovation, problem-solving and leadership in the industry. Let’s build a tech future that reflects the diversity of our society. https://bit.ly/3UNtOCh

How do you know if your strategy is clear going into this year? If your team can recall it and align around it. But that is really hard to do - you may have spent a lot of time coming up with a strategy but if you ask your team to explain the strategy and they come up with different answers, then your job is not done. Alignment > Strategy. At HubSpot, we begin every year by laying out our strategy on a page and spend a lot of time aligning cross-functional teams around that. We start with our mission. That’s our “why.” The reason we are excited to come to work. For us it is to help small and medium businesses grow. Mission is typically a long-term “why”. It may change every decade or two. Our mission informs our strategy. It defines what we intend to do to achieve our mission. A strategy shouldn’t change dramatically every year. Our priorities stem from our strategy. What are the most important things we need to accomplish this year to execute our strategy? Priorities should be specific and measurable. And the way you measure them is via clear outcomes.  These are the key metrics we measure and the targets we set for the year. They tell us when we’re on track and when we need to course-correct. But don't forget the omissions. These are the things we are explicitly not going to do. They are often good ideas that are tempting to pursue, but not where we should focus this year. Mission → Strategy → Priorities → Outcomes (and Omissions). Simple. When you can explain your strategy on a single page, it becomes much easier to keep everyone on the same page – whether you’re a small startup or a company like HubSpot. How do you align your teams to strategy as you start this year?

"I like my job and my company, but my salary doesn’t feel right". Aisha had been working in her company for three years. She enjoyed her work. Her team liked her. Her manager was supportive. But every time she saw her salary, she felt unhappy. “I’m doing more work now, but my salary is still the same,” she thought. This happens to many people. They’re happy with their company, but not with their pay. Aisha decided to take it up. Here’s what she did (and what you can learn too): 1. She did her research. Aisha checked online to see what others in her role were earning. She made sure her salary request was fair. 2. She picked the right time. She didn’t just ask suddenly. She booked a proper meeting with her manager—at a time when things were calm at work. 3. She made a list of her work. She wrote down her achievements: A process she improved Clients she helped keep happy Extra tasks she had taken on This showed how she was helping the company grow. 4. She knew what to ask for. Aisha had a clear number in mind. Not too high, not too low—just right for her skills and work. 5. She practiced what to say. She talked through her points with a friend first, so she could speak clearly and with confidence. 6. She stayed calm and polite. During the meeting, she didn’t complain or compare. She simply explained her work and asked for a raise. 7. She talked about the future. Aisha also shared her plans to keep learning and doing even better work in the company. 8. She was ready to talk it out. Her manager didn’t agree right away. There was some back-and-forth. Aisha listened and stayed open to different options, like bonuses or new projects. 9. She followed up. After the meeting, she said thank you. This showed she respected her manager’s time. 📌 What happened next? A few weeks later, Aisha got a raise—and a new opportunity at work. 💡 What can we learn? If you like your job but feel underpaid, don’t stay silent. Make a plan, stay professional, and speak up—just like Aisha did. Hope you have liked the article on how to ask for Salary Increment. Follow Me Smriti Gupta for Career & Resume tips #salarynegotiation #career #leadership

Grid bottlenecks are a feature — not a bug — of the energy transition. For years, we viewed economics as the main hurdle to scaling clean energy. High costs for wind, solar, heat pumps, and storage dominated the conversation. But the world has changed. Thanks to extraordinary innovation and dramatic cost reductions in renewables and electrification technologies, the bottlenecks we face today are different. They’re no longer about whether clean energy is affordable — it is. Instead, the challenge is whether our energy systems can evolve quickly enough to integrate it. A recent Financial Times piece highlights this clearly: across Europe, the rapid build-out of renewable generation now outpaces the ability of grids to move electricity to where it’s needed. Curtailment, congestion, and long queues for grid connections already cost billions annually — and without decisive action, these costs will grow. This isn’t a sign of failure. It’s a sign of success. It means the transition is happening faster than the infrastructure built for the fossil era can handle. The rise of decentralised, variable renewables and electrified heating and transport requires a fundamentally different approach to planning — one that anticipates growth rather than reacts to it. The EU’s move toward more coordinated, top-down scenario building and cross-border grid planning recognises exactly this. Better alignment between countries and system operators, faster permitting, and prioritisation of critical projects are essential steps to unlock the full value of cheap clean energy. Because every euro lost to bottlenecks is not a cost of climate action — it’s a cost of not modernising our grids fast enough. The more successful we are in deploying renewables and electrification, the more urgently we must upgrade and expand our grids. Grid constraints are not a reason to slow down. They’re a reason to speed up the transformation of an energy system that was never designed for the technologies now powering our transition.

𝗠𝗮𝗴𝗻𝗶𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗼𝘃𝗲𝗿𝘃𝗶𝗲𝘄 𝗼𝗳 𝗮𝗻 𝗲𝗹𝗲𝗰𝘁𝗿𝗶𝗰𝗮𝗹 𝘀𝘂𝗯𝘀𝘁𝗮𝘁𝗶𝗼𝗻 Substations are used at the generation, transmission, and distribution levels. Generators (at various power plants) generally produce electricity at lower voltages. However, these lower voltages are not efficient for long-distance transmission primarily due to technical losses (such as power loss (I^2*R) or voltage drops). This is because the current is higher at a lower voltage for the same amount of power transmitted. This contributes to huge losses (I^2*R), where "I" is the load current and "R" is the line's resistance. A transmission substation is used to step up the generation voltage for long-distance delivery to reduce losses. Most power generation facilities are located far from customers (homes, businesses, and commercial or industrial electricity consumers). A transmission line length is considered: ✅ Short if it's less than or equal to 𝟱𝟬 𝗺𝗶𝗹𝗲𝘀 (𝗼𝗿 𝟴𝟬 𝗸𝗺).  ✅ Medium if it's greater than 𝟱𝟬 𝗺𝗶𝗹𝗲𝘀 (𝟴𝟬 𝗸𝗺) but less than or equal to 𝟭𝟱𝟬 𝗺𝗶𝗹𝗲𝘀 (𝟮𝟰𝟭 𝗸𝗺) ✅ Long if it's greater than 𝟭𝟱𝟬 𝗺𝗶𝗹𝗲𝘀 (𝟮𝟰𝟭 𝗸𝗺) The distribution substation takes the power from a transmission or sub-transmission substation and further steps down the voltages for distribution. For instance, a solar PV power plant is a generator. An inverter(s) is/are needed to convert the DC power from the solar panels to AC power before injecting it into a distribution or transmission network. Let's assume the expected power to be delivered is 2 MVA, and we have one central inverter at 600 V. The load current (I) at 600 V will be (𝟮 𝘅 𝟭𝟬^𝟲)/(𝟭.𝟳𝟯𝟮*𝟲𝟬𝟬) = 𝟭𝟵𝟮𝟱 𝗔. For simplicity, let's assume a conductor resistance of 0.5 ohms (keep constant) Power loss = 𝟭𝟵𝟮𝟱*𝟭𝟵𝟮𝟱*𝟬.𝟱 = 𝟭,𝟴𝟱𝟮,𝟴𝟭𝟮 𝗪 A load current of 1925 A is large, so we must buy large conductors and associated support systems to transport the 2 MVA apparent power. The technical losses and voltage drops at this current are significant and uneconomical. A transformer is used to transform the 600 V to say 34,500 V, and the current at such medium voltage will be: (𝟮 𝘅 𝟭𝟬^𝟲)/(𝟭.𝟳𝟯𝟮*𝟯𝟰,𝟱𝟬𝟬) = 𝟯𝟯 𝗔 and power loss 𝟯𝟯*𝟯𝟯*𝟬.𝟱 = 𝟱𝟰𝟱 𝗪 Same power, but now, we have a smaller load current to evacuate through a distance. For long distances and larger power, it's even more economical to step up the 34,500 V to a transmission level, say 115,000 V. At 115,000 V, the transferred current is further reduced to: (𝟮 𝘅 𝟭𝟬^𝟲)/(𝟭.𝟳𝟯𝟮*𝟭𝟭𝟱,𝟬𝟬𝟬) = 𝟭𝟬 𝗔.  and power loss is 𝟭𝟬*𝟭𝟬*𝟬.𝟱 = 𝟱𝟬 𝗪 These assumptions give a better perspective on the discussion. But remember that an increase in voltage will require you to consider factors such as increasing the cost of equipment insulation. A lot happens between these systems, so it can't be explained in this limited space. This is just an overview. 📹 Surdu Alexandru Andrei

During my time serving in government, I saw firsthand how geopolitics can impact energy production and flows, with cascading impacts on market and macroeconomic trends.   We're already seeing this play out following the last few days in the Middle East. U.S. and Israeli strikes on Iran triggered retaliatory action across the region that has disrupted energy production and transit.   The market reaction is changing quickly. Since I recorded this video on Monday, oil and gas prices have jumped further, and equities have shifted toward a risk-off move as investors price in continued escalation. Bonds sold off further, reflecting inflation fears in developed markets. Due to the segmented nature of natural gas markets, the impact of higher prices will hit regions differently, with Europe more exposed than the U.S. to elevated LNG prices.   The central question: will this remain a short-term volatility spike or evolve into a broader supply shock? The duration of the disruption and the severity of transit impacts are the core variables I'm watching.   ⬇️ Watch the full video for my latest take on what this could mean for markets.