Impact of AI Development

AI holds great potential for the semiconductor industry and will kick-start the next round of innovation for faster, cheaper and more energy-efficient computation – that was my message today at SPIE Advanced Lithography + Patterning. I discussed the potential and the challenges that AI holds for our industry.   The potential is clearly huge. AI is rapidly integrated into applications, and high-performance compute is expected to underpin growth towards $1 trillion of semiconductor sales by 2030. The challenges are around the computing needs of AI models and related energy consumption. The compute workload of training a leading AI model has increased 16x every 2 years in recent years – much faster than the increase in computing power delivered by Moore’s law, which is about 2x every 2 years. The energy needed to train a leading model has not grown so steeply but still rose 10x every 2 years. This computing need has been met by building supercomputers and massive data centers. If you extrapolate these trends, training a leading AI model would need the entire world-wide electricity supply in about 10 years. That’s clearly not realistic, so the trend has to break, by training algorithms becoming more efficient and by chips becoming more efficient. In other words, the needs of AI will stimulate immense innovation in chip design and manufacturing – and the potential value of AI to our society will put urgency and funding behind that drive. As a consequence, chip makers are pulling all levers to accelerate semiconductor scaling. This includes lithographic “2D” scaling: shrinking the dimensions of transistors to pack more into a square millimeter. It will also include “3D” integration, with innovations like backside power delivery, transistor designs like gate-all-around, as well as stacking chips in the package, where holistic lithography will play a critical role to deliver performance requirements. ASML will support these trends through a comprehensive, holistic lithography portfolio. Our 0.33 NA/0.55 NA EUV lithography systems allow chip makers to shrink dimensions at the lowest possible cost on their critical layers, while tightly matched and highly productive DUV systems will continue to reduce cost. More than ever, metrology and inspections tools – whose data is fed into lithography control solutions that keep the patterning process operating within tight specs to deliver the highest possible production yields – will be essential to deliver 2D scaling and 3D integration processes. 3D integration requires wafer-to-wafer bonding, and we have demonstrated the capability to map the stresses and distortions that bonding creates and to compensate for them, reducing overlay errors for post-bonding patterning by 10x or more.   It was a pleasure catching up with the industry’s lithography and patterning experts in San Jose. I’m excited to see our collective innovation power having a go at these challenges. Together, we will push technology forward.

AI is no longer just decorating rooms. It’s redesigning how we live. AI can now rethink rooms, floors, and entire layouts—turning bold ideas into build-ready designs. Would you do floor like that? The data behind the shift: • 30–50% faster design cycles using generative layout tools • 100+ layout permutations generated from a single brief • Up to 20–30% improvement in space utilization • 10–25% energy savings when airflow, lighting, and thermal paths are simulated early • 40% fewer late-stage design changes thanks to digital testing What’s fundamentally different? AI treats floor plans like software systems: Pedestrian movement is simulated before construction Natural light and ventilation are optimized virtually Furniture, walls, and utilities are stress-tested digitally Cost, carbon footprint, and materials are optimized in parallel This enables: Smaller homes that feel larger Offices designed around productivity and wellbeing Buildings that adapt over time instead of aging poorly The biggest myth? AI replaces architects and designers. Reality: AI handles complexity and permutations. Humans focus on vision, culture, emotion, and identity. The future of architecture isn’t just smart. It’s generative, data-driven, and human-centric. #AI #Architecture #Design via @Visual Spaces Lab #PropTech #GenerativeAI #FutureOfLiving #SmartBuildings #Innovation

The Alan Turing Institute 𝗮𝗻𝗱 the LEGO Group 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗰𝗵𝗶𝗹𝗱-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗔𝗜 𝘀𝘁𝘂𝗱𝘆! ⬇️ (𝘈 𝘮𝘶𝘴𝘵-𝘳𝘦𝘢𝘥 — 𝘦𝘴𝘱𝘦𝘤𝘪𝘢𝘭𝘭𝘺 𝘪𝘧 𝘺𝘰𝘶 𝘩𝘢𝘷𝘦 𝘤𝘩𝘪𝘭𝘥𝘳𝘦𝘯.) While most AI debates and studies focus on models, chips, and jobs — this one zooms in on something far more personal: 𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗵𝗲𝗻 𝗰𝗵𝗶𝗹𝗱𝗿𝗲𝗻 𝗴𝗿𝗼𝘄 𝘂𝗽 𝘄𝗶𝘁𝗵 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜? They surveyed 1,700+ kids, parents, and teachers across the UK — and what they found is both powerful and concerning. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 9 𝘁𝗵𝗶𝗻𝗴𝘀 𝘁𝗵𝗮𝘁 𝘀𝘁𝗼𝗼𝗱 𝗼𝘂𝘁 𝘁𝗼 𝗺𝗲 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼𝗿𝘁: ⬇️ 1. 1 𝗶𝗻 4 𝗸𝗶𝗱𝘀 (8–12 𝘆𝗿𝘀) 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘂𝘀𝗲 𝗚𝗲𝗻𝗔𝗜 — 𝗺𝗼𝘀𝘁 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘀𝗮𝗳𝗲𝗴𝘂𝗮𝗿𝗱𝘀 → ChatGPT, Gemini, and even MyAI on Snapchat are now part of daily digital play. 2. 𝗔𝗜 𝗶𝘀 𝗵𝗲𝗹𝗽𝗶𝗻𝗴 𝗸𝗶𝗱𝘀 𝗲𝘅𝗽𝗿𝗲𝘀𝘀 𝘁𝗵𝗲𝗺𝘀𝗲𝗹𝘃𝗲𝘀 — 𝗲𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗹𝘆 𝘁𝗵𝗼𝘀𝗲 𝘄𝗶𝘁𝗵 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗻𝗲𝗲𝗱𝘀 → 78% of neurodiverse kids use ChatGPT to communicate ideas they struggle to express otherwise. 3. 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆 𝗶𝘀 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 — 𝗯𝘂𝘁 𝗻𝗼𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗶𝗻𝗴 → Kids still prefer offline tools (arts, crafts, games), even when they enjoy AI-assisted play. Digital is not (yet) the default. 4. 𝗔𝗜 𝗮𝗰𝗰𝗲𝘀𝘀 𝗶𝘀 𝗵𝗶𝗴𝗵𝗹𝘆 𝘂𝗻𝗲𝗾𝘂𝗮𝗹 → 52% of private school students use GenAI, compared to only 18% in public schools. The next digital divide is already here. 5. 𝗖𝗵𝗶𝗹𝗱𝗿𝗲𝗻 𝗮𝗿𝗲 𝘄𝗼𝗿𝗿𝗶𝗲𝗱 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜’𝘀 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 𝗶𝗺𝗽𝗮𝗰𝘁 → Some kids refused to use GenAI after learning about water and energy costs. Let that sink in. 6. 𝗣𝗮𝗿𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝘁𝗶𝗰 — 𝗯𝘂𝘁 𝗱𝗲𝗲𝗽𝗹𝘆 𝘄𝗼𝗿𝗿𝗶𝗲𝗱 → 76% support AI use, but 82% are scared of inappropriate content and misinformation. Only 41% fear cheating. 7. 𝗧𝗲𝗮𝗰𝗵𝗲𝗿𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 — 𝗮𝗻𝗱 𝗹𝗼𝘃𝗶𝗻𝗴 𝗶𝘁 → 85% say GenAI boosts their productivity, 88% feel confident using it. They’re ahead of the curve. 8. 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗶𝘀 𝘂𝗻𝗱𝗲𝗿 𝘁𝗵𝗿𝗲𝗮𝘁 → 76% of parents and 72% of teachers fear kids are becoming too trusting of GenAI outputs. 9. 𝗕𝗶𝗮𝘀 𝗮𝗻𝗱 𝗶𝗱𝗲𝗻𝘁𝗶𝘁𝘆 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝘀𝘁𝗶𝗹𝗹 𝗮 𝗯𝗹𝗶𝗻𝗱𝘀𝗽𝗼𝘁 → Children of color felt less seen and less motivated to use tools that didn’t reflect them. Representation matters. The next generation isn’t just using AI. They’re being shaped by it. That’s why we need a more focused, intentional approach: Teaching them not just how to use these tools — but how to question them. To navigate the benefits, the risks, and the blindspots. 𝗪𝗮𝗻𝘁 𝗺𝗼𝗿𝗲 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻𝘀 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀? Subscribe to Human in the Loop — my new weekly deep dive on AI agents, real-world tools, and strategic insights: https://lnkd.in/dbf74Y9E

Using light as a neural network, as this viral video depicts, is actually closer than you think. In 5-10yrs, we could have matrix multiplications in constant time O(1) with 95% less energy. This is the next era of Moore's Law. Let's talk about Silicon Photonics... The core concept: Replace electrical signals with photons. While current processors push electrons through metal pathways, photonic systems use light beams, operating at fundamentally higher speeds (electronic signals in copper are 3x slower) with minimal heat generation. It's way faster. While traditional chips operate at 3-5 GHz, photonic devices can achieve >100 GHz switching speeds. Current interconnects max out at ~100 Gb/s. Photonic links have demonstrated 2+ Tb/s on a single channel. A single optical path can carry 64+ signals. It's way more energy efficient. Current chip-to-chip communication costs ~1-10pJ/bit. Photonic interconnects demonstrate 0.01-0.1pJ/bit. For data centers processing exabytes, this 200x improvement means the difference between megawatt and kilowatt power requirements. The AI acceleration potential is revolutionary. Matrix operations, fundamental to deep learning, become near-instantaneous: Traditional chips: O(n²) operations. Photonic chips: O(1) - parallel processing through optical interference. 1000×1000 matmuls in picoseconds. Where are we today? Real products are shipping: — Intel's 400G transceivers use silicon photonics. — Ayar Labs demonstrates 2Tb/s chip-to-chip links with AMD EPYC processors. Performance scales with wavelength count, not just frequency like traditional electronics. The manufacturing challenges are immense. — Current yield is ~30%. Silicon's terrible at emitting light and bonding III-V materials to it lowers yield — Temp control is a barrier. A 1°C change shifts frequencies by ~10GHz. — Cost/device is $1000s To reach mass production we need: 90%+ yield rates, sub-$100 per device costs, automated testing solutions, and reliable packaging techniques. Current packaging alone can cost more than the chip itself. We're 5+ years from hitting these targets. Companies to watch: ASML (manufacturing), Intel (data center), Lightmatter (AI), Ayar Labs (chip interconnects). The technology requires major investment, but the potential returns are enormous as we hit traditional electronics' physical limits.

When I have a conversation about AI with a layperson, reactions range from apocalyptic fears to unrestrained enthusiasm. Similarly, with the topic of whether to use synthetic data in corporate settings, perspectives among leaders vary widely. We're all cognizant that AI systems rely fundamentally on data. While most organizations possess vast data repositories, the challenge often lies in the quality rather than the quantity. A foundational data estate is a 21st century competitive advantage, and synthetic data has emerged as an increasingly compelling solution to address data quality in that estate. However, it raises another question. Can I trust synthetic more or less than experiential data? Inconveniently, it depends on context. High-quality data is accurate, complete, and relevant to the purpose for which its being used. Synthetic data can be generated to meet these criteria, but it must be done carefully to avoid introducing biases or inaccuracies, both of which are likely to occur to some measure in experiential data. Bottom line, there is no inherent hierarchical advantage between experiential data (what we might call natural data) and synthetic data—there are simply different characteristics and applications. What proves most trustworthy depends entirely on the specific context and intended purpose. I believe both forms of data deliver optimal value when employed with clarity about desired outcomes. Models trained on high-quality data deliver more reliable judgments on high impact topics like credit worthiness, healthcare treatments, and employment opportunities, thereby strengthening an organization's regulatory, reputational, and financial standing. For instance, in a recent visit a customer was grappling with a relatively modest dataset. They wanted to discern meaningful patterns within their limited data, concerned that an underrepresented data attribute or pattern might be critical to their analysis. A reasonable way of revealing potential patterns is to augment their dataset synthetically. The data set would maintain statistical integrity (the synthetic mimics the statistical properties and relationships of the original data) allowing any obscure patterns to emerge with clarity. We’re finding this method particularly useful for preserving privacy, identifying rare diseases or detecting sophisticated fraud. As we continue to proliferate AI across sectors, senior leaders must know it's not all "upside." Proper oversight mechanisms to verify that synthetic data accurately represents real-world conditions without introducing new distortions is a must. However, when approached with "responsible innovation" in mind, synthetic data offers a powerful tool for enabling organizations to augment limited datasets, test for bias, and enhance privacy protections, making synthetic data a competitive differentiator. #TrustworthyAI #ResponsibleInnovation #SyntheticData

As we reach the midpoint of 2026, one thing has become increasingly clear: the conversation around AI has shifted. Again.    A year ago, much of the discussion centered on what's possible. Today, the focus is on what's practical: how we build the data foundations, governance, infrastructure, and workforce capabilities needed to create lasting value.    A few key learnings we're applying as we head into the second half of the year:  --AI transformation is ultimately a people transformation. Technology is advancing quickly, but realizing its potential means building a workforce that knows how to use it thoughtfully.  --AI fluency is a core capability. That means thoughtfully redesigning roles and workflows and giving employees the confidence to incorporate AI into their daily work while maintaining human judgment and accountability.  --The role of technology leaders is no longer focused only on delivering systems. We're helping shape enterprise strategy, building workforce capabilities, and creating the conditions for innovation to scale responsibly.    The pace of change isn't slowing down, but some principles remain constant: start with the problem you're trying to solve, invest in your people as much as your platforms, build governance from the beginning, and remember that every technology decision should ultimately create value for the people we serve.

MIT Unveils AI Chip That Operates Entirely on Light, Not Electricity Researchers at MIT have created a revolutionary AI accelerator chip that performs computations entirely using light rather than electricity potentially slashing energy consumption in data centers by over 90%. This photonic AI chip leverages arrays of nano-optic waveguides and micro-ring modulators to process data using beams of modulated light. At its core, the chip replaces electrical transistors with tiny optical interference units that manipulate light’s phase and amplitude. Matrix multiplications, the backbone of neural networks, are executed as light passes through a mesh of these units, eliminating resistive heating entirely. The chip has no moving parts and transmits information at the speed of light, literally. Initial tests showed the photonic processor performing convolutional neural network (CNN) tasks at 10 teraflops per watt far surpassing Nvidia’s top-tier GPUs. What’s more, it generates no heat beyond the laser source itself, drastically simplifying cooling and thermal design. MIT’s prototype uses silicon photonics and is fully compatible with existing CMOS processes, making it scalable for commercial production. Future versions may be paired with on-chip photonic memory, enabling entirely light-driven inference systems. The team envisions hyperscale data centers running vast language models on these chips with almost no electricity use, ushering in a post-electronic computing era. Note: The opinions expressed here are solely my own and do not represent my employer.

🚀 Google just dropped the blueprint for the future of agentic AI: Context Engineering, Sessions & Memory. If prompt engineering was about crafting good questions, context engineering is about building an AI’s entire mental workspace. Here’s why this paper matters 👇 What’s Context Engineering? LLMs are stateless, they forget everything between calls. 🔹Context engineering turns them into stateful systems by dynamically assembling: • System instructions (the “personality” of the agent) • External knowledge (RAG results, tools, and outputs) • Session history (ongoing dialogue) • Long-term memory (summaries and facts from past sessions) • It’s not prompt design anymore, it’s prompt orchestration. Think of sessions as your workbench, messy but active. Sessions manage short-term context and working memory. Think of memory as your filing cabinet, organized, persistent, and searchable. Memories persist facts, preferences, and strategies across time and agents. Together, they make AI personal, consistent, and self-improving. My Takeaways: Context is the new compute, your system’s intelligence depends on what it sees, not just the model you use. Memory isn’t a vector DB, it’s an LLM-driven ETL pipeline that extracts, consolidates, and prunes knowledge. Multi-agent systems need shared memory layers, not shared prompts. Procedural memory (the how) is the next frontier, agents learning strategies, not just storing facts. Building an “agent” today isn’t about chaining APIs together. It’s about context architecture to make models actually think across time. The future of AI won’t belong to those who fine-tune models, it’ll belong to those who engineer context. “Stateful AI begins with context engineering.” This might just be the new foundation of agentic systems.

A 12-year-old told an AI chatbot her secrets… because she had no one else to tell. When I was a kid, I was lonely. I didn’t have many friends. After school, my “conversations” were with my sheep and chickens. I’d pour my heart out, and in my mind, they answered back. If AI chatbots had existed back then? I probably would have turned to one instead. And nothing scares me more as a mom than the idea of my kids thinking AI is a better listener than I am. Because today’s kids are doing exactly that. A new report on kids and AI chatbots found: - 23% have sought advice from AI chatbots. - 35% say it feels like talking to a friend (50% among vulnerable kids). - 12% talk to AI chatbots because they have no one else (23% among vulnerable kids). - 40% have no concerns about following chatbot advice. This isn’t just about technology. It’s about loneliness. Trust. And the gap AI is filling… a gap that should be filled by humans. Why it’s alarming: - There are no guardrails tailored for kids. - Adult supervision is rare (or nonexistent). - Kids can’t always spot bad advice or manipulation. - Vulnerable kids are the most likely to form unhealthy “relationships” with AI bots. I’ve said it before: I don’t recommend children use AI chatbots unless an adult is actively involved. The risk of emotional dependence, harmful content, and subtle manipulation is too high. If we don’t start AI literacy for kids now, we’ll wake up to a generation who think AI is their best friend, their therapist, and their guide to life. That’s not just a tech problem. It’s a human risk problem. Parents. Educators. Leaders. If AI is raising our kids, we’ve already failed them. (Report: “Me, Myself & AI: Understanding and safeguarding children’s use of AI chatbots.”) #AILiteracy #ChildSafety #HumanRisk #DigitalParenting #AI #CyberSecurity #DigitalWellness

Generating Synthetic Data: A Simple Guide Synthetic data generation is critical in building AI models where your data is sparse. Here we're not just filling gaps, we're carefully crafting data to make models smarter, more robust, and safer. Just like in cooking, you have different recipes for different goals. Here’s a simple breakdown of synthetic data generation methods 1. Generative Synthesis What it is: Letting a powerful AI (like a large language or image model) invent completely new examples from scratch based on a description or a set of rules. Good for: Generating massive amounts of novel data quickly. Watch out for: The AI can "hallucinate" and create nonsense or get stuck in a repetitive style. 2. Transformation & Rephrasing What it is: Taking existing, real data and altering it while keeping its core meaning. Think paraphrasing a sentence, swapping words, or changing an image's color. Good for: A cheap and safe way to make your dataset more diverse. Watch out for: Small changes can sometimes accidentally change the data's true label, so you need to double-check. 3. Programmatic Labeling & Distillation What it is: Using a smarter, more powerful AI (the "teacher") to label a bunch of unlabeled or messy data for a smaller, simpler AI (the "student") to learn from. Good for: Quickly creating labeled datasets at a huge scale. Watch out for: The student AI will inherit all the blind spots and biases of its teacher if you're not careful. 4. Agentic Self-Play What it is: Having multiple AI "agents" interact with each other or a simulated environment to create complex, multi-step data. This is perfect for generating conversations, tool-use sequences, or strategic game-play. Good for: Teaching AI how to perform long, complicated tasks. Watch out for: The AIs can learn to "cheat" the simulation or develop weird, unrealistic strategies that don't work in the real world. 5. Adversarial & Safety Data What it is: Intentionally creating tricky, confusing, or malicious data to find and fix your AI's weak spots. This is like a quality control check. Good for: Making your AI more robust, secure, and safe before it's deployed. Watch out for: You have to be very creative to think of all the ways things can go wrong. No matter which method you use, you need a strict quality control layer. This involves: Removing Duplicates: So the AI doesn't see the same example over and over. Scrubbing Sensitive Info: Filtering out personal data or offensive content. Tracking Lineage: Knowing exactly how and where a piece of synthetic data was created. The real thing isn't in any single technique, but in knowing which combination to use for your specific goal. Start simple, measure what works, and never compromise on data quality. After all, your AI will only ever be as good as the data it sees. #AI