Impact of AI Development

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

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.

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.

My Harvard Thesis: AI Isn’t Just Replacing Jobs, It’s Breaking How We Build Expertise For decades, every technological revolution has followed a familiar narrative: jobs are displaced, new ones are created, and the workforce adapts. Based on my Harvard thesis, Artificial intelligence is different. We discovered that the real risk isn’t job loss per se, because what if we get past the short-term deficit. The real risk we uncoverd is structural, AI is dismantling the very pathways through which human expertise is built. Every company, whether it realizes it or not, relies on a simple pipeline: Entry-level work → Skill development → Expertise → Leadership 💡 Entry-level roles have never just been about productivity. They’ve been about learning—absorbing context, building judgment, and developing intuition. 💡 AI however is automating those tasks. The work we used to learn from is being automated. So while the competitive pressures have us focusing on productivity gains, there's a second-order effect emerging beneath the surface. As AI automates foundational tasks faster than humans can develop expertise, a gap forms. We call this the "Talent Formation Fracture" 🧠 - the rate at which work is automated will exceed the rate at which humans become experts, threatening our 'Human Expert in the Loop' governance protocol in 5 years. At first, nothing will appear broken. Productivity improves. Costs decrease. Output accelerates. But over time, the supply of people who actually understand the work will be fewer. Without a continuous pipeline of developing talent, companies will face: 👉 Fewer future leaders 👉 Reduced ability to validate AI outputs 👉 Increased operational risk Ironically, the more companies rely on AI, the more they will depend on human expertise they are no longer cultivating. To address this, organizations must redesign how work is structured. I will provide more in other posts, but we have recommendations on how to conduct: 👉 Talent Planning in the AI Era 👉 What the future of work will look like in the next 2 years 👉 How roles will evolve We discuss these concepts in the podcast with Brett A. Hurt! It's a 2 part series take a listen. You'll walk away with some really important things.

🚀 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.

🚨 It's 2025, but many lawyers are still making the SAME MISTAKES while using AI. Here's the latest case and what EVERY lawyer should know: Last week, lawyers representing a family in a lawsuit against Walmart and Jetson Electric Bikes admitted to using AI after the judge said nearly ALL cases cited did not exist. The judge wrote: "Plaintiffs cited nine total cases: (...) The problem with these cases is that none exist, except (...). The cases are not identifiable by their Westlaw cite, and the Court cannot locate the District of Wyoming cases by their case name in its local Electronic Court Filing System. Defendants aver through counsel that 'at least some of these mis-cited cases can be found on ChatGPT.' [ECF No. 150] (providing a picture of ChatGPT locating “Meyer v. City of Cheyenne” through the fake Westlaw identifier). Additionally, some of Plaintiffs’ language used for explaining the “Legal Standard” is peculiar. (...)" The lawyers then answered: "The cases cited in this Court’s order to show cause were not legitimate. Our internal AI platform 'hallucinated' the cases in question while assisting our attorney in drafting the motion in limine. This matter comes with great embarrassment and has prompted discussion and action regarding the training, implementation, and future use of artificial intelligence within our firm. This serves as a cautionary tale for our firm and all firms, as we enter this new age of AI." → My comments: 1. Lawyers will always be FULLY RESPONSIBLE for the legal work they perform. "Our AI system hallucinated" will never be accepted as a legal excuse (it's the equivalent of a child saying "my dog ate my homework" at school). Lawyers should consider that when opting to use AI to perform any legal work (including reviewing, researching, drafting, etc.). 2. It's bad for any lawyer or law firm's reputation to admit that they didn't review the legal work they were paid to do (and let the AI system do it instead). Law firms that have an open and lenient AI policy are taking high risks. 3. A reminder that ALL existing generative AI applications have some rate of hallucinations, meaning that their developers can't promise that the outcomes will be 100% accurate or based on factual sources. On the other hand, lawyers are paid, among other things, to provide accurate legal advice grounded in evidence and factual knowledge. Any AI company that has legal professionals as their target audience should have that in mind. 4. General-purpose AI systems like ChatGPT - without any additional guardrails or fine-tuning that consider the peculiarities of legal work - are likely not suitable for legal professionals and should be avoided. ♻️ If you have lawyers in your network, share it with them. 👉 NEVER MISS my AI governance updates [especially if you are a lawyer!]: join 52,600+ readers who receive my weekly newsletter (subscribe below). #AI #AIGovernance #Law #AIRegulation #Lawyers #AIPolicy #LegalWork

"The rapid evolution and swift adoption of generative AI have prompted governments to keep pace and prepare for future developments and impacts. Policy-makers are considering how generative artificial intelligence (AI) can be used in the public interest, balancing economic and social opportunities while mitigating risks. To achieve this purpose, this paper provides a comprehensive 360° governance framework: 1 Harness past: Use existing regulations and address gaps introduced by generative AI. The effectiveness of national strategies for promoting AI innovation and responsible practices depends on the timely assessment of the regulatory levers at hand to tackle the unique challenges and opportunities presented by the technology. Prior to developing new AI regulations or authorities, governments should: – Assess existing regulations for tensions and gaps caused by generative AI, coordinating across the policy objectives of multiple regulatory instruments – Clarify responsibility allocation through legal and regulatory precedents and supplement efforts where gaps are found – Evaluate existing regulatory authorities for capacity to tackle generative AI challenges and consider the trade-offs for centralizing authority within a dedicated agency 2 Build present: Cultivate whole-of-society generative AI governance and cross-sector knowledge sharing. Government policy-makers and regulators cannot independently ensure the resilient governance of generative AI – additional stakeholder groups from across industry, civil society and academia are also needed. Governments must use a broader set of governance tools, beyond regulations, to: – Address challenges unique to each stakeholder group in contributing to whole-of-society generative AI governance – Cultivate multistakeholder knowledge-sharing and encourage interdisciplinary thinking – Lead by example by adopting responsible AI practices 3 Plan future: Incorporate preparedness and agility into generative AI governance and cultivate international cooperation. Generative AI’s capabilities are evolving alongside other technologies. Governments need to develop national strategies that consider limited resources and global uncertainties, and that feature foresight mechanisms to adapt policies and regulations to technological advancements and emerging risks. This necessitates the following key actions: – Targeted investments for AI upskilling and recruitment in government – Horizon scanning of generative AI innovation and foreseeable risks associated with emerging capabilities, convergence with other technologies and interactions with humans – Foresight exercises to prepare for multiple possible futures – Impact assessment and agile regulations to prepare for the downstream effects of existing regulation and for future AI developments – International cooperation to align standards and risk taxonomies and facilitate the sharing of knowledge and infrastructure"

Last month's World Economic Forum's Annual Meeting in Davos served as a crucial platform for leaders to engage in meaningful discussions surrounding the complexities of #GenAI.  I’ll devote the next few weeks to addressing these critical questions and hearing your thoughts on AI topics that need attention.         First, I’m exploring whether AI will catalyze inclusive growth or contribute to greater societal inequality. As we stand at the beginning of the #generativeAI era, our choices about ethical AI development, government oversight, and equal access and distribution of its benefits will set the tone for how businesses operate and how we live and interact with each other.          Drawing historical parallels makes it easy to see how innovation impacts societal gaps. For example, the Industrial Revolution deepened societal inequalities as the elite benefited while the lower class endured harsh working conditions. However, trade unions intervened to support workers’ rights, leading to more inclusive growth.      Similarly, a "digital divide" emerged in the Digital Revolution, with a small segment of the population reaping disproportionate benefits. But the benefit extended to all as governments facilitated more equitable access.       As we navigate the GenAI era, however, there is reason for optimism.  1. Ubiquity of smartphone access (w/ connectivity) brings AI to every user across the developed and developing world, instantly.  2. The emerging world stands to gain more quickly as a chronic shortage of teachers and medical professionals can be compensated for, leading to healthier and better educated populations, thus shrinking the human capital divide.  3. Governments, civic and non-profit agencies can have access to almost real time data about people in need, thus helping provide better citizen services.  4. Across most job types, GenAI is more likely to “augment” than “replace” leading to more productivity-driven GDP growth than we’ve seen in the last few decades. This should lead to inclusivity if governments distribute the benefits more equally.    The key factors in ensuring inclusive growth are equal access, equitable distribution of benefits (including through taxation), ethical development (free of bias) and responsible usage. Governments will have a huge part to play by ensuring these through optimal policies and regulation.     At NTT, we believe in the democratization of GenAI. We envision a future where a variety of special purpose LLMs operate in concert with each other on cheap and power efficient infrastructure (vs. a few general purpose LLMs with a winner-takes-all dynamic). To that end, we are innovating to enable low cost, low power consumption infrastructure IOWN, and have developed our very light weight and power efficient LLM (Tsuzumi) that can inter-operate with other LLMs.  Do you think GenAI will be the great equalizer? I’d love to hear what you’re doing to ensure the technology drives inclusive growth.   

A nice review article "Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation" covers the scope of tools and approaches for how AI can support science. Some of areas the paper covers: (link in comments) 🔎 Literature search and summarization. Traditional academic search engines rely on keyword-based retrieval, but AI-powered tools such as Elicit and SciSpace enhance search efficiency with semantic analysis, summarization, and citation graph-based recommendations. These tools help researchers sift through vast scientific literature quickly and extract key insights, reducing the time required to identify relevant studies. 💡 Hypothesis generation and idea formation. AI models are being used to analyze scientific literature, extract key themes, and generate novel research hypotheses. Some approaches integrate structured knowledge graphs to ground hypotheses in existing scientific knowledge, reducing the risk of hallucinations. AI-generated hypotheses are evaluated for novelty, relevance, significance, and verifiability, with mixed results depending on domain expertise. 🧪 Scientific experimentation. AI systems are increasingly used to design experiments, execute simulations, and analyze results. Multi-agent frameworks, tree search algorithms, and iterative refinement methods help automate complex workflows. Some AI tools assist in hyperparameter tuning, experiment planning, and even code execution, accelerating the research process. 📊 Data analysis and hypothesis validation. AI-driven tools process vast datasets, identify patterns, and validate hypotheses across disciplines. Benchmarks like SciMON (NLP), TOMATO-Chem (chemistry), and LLM4BioHypoGen (medicine) provide structured datasets for AI-assisted discovery. However, issues like data biases, incomplete records, and privacy concerns remain key challenges. ✍️ Scientific content generation. LLMs help draft papers, generate abstracts, suggest citations, and create scientific figures. Tools like AutomaTikZ convert equations into LaTeX, while AI writing assistants improve clarity. Despite these benefits, risks of AI-generated misinformation, plagiarism, and loss of human creativity raise ethical concerns. 📝 Peer review process. Automated review tools analyze papers, flag inconsistencies, and verify claims. AI-based meta-review generators assist in assessing manuscript quality, potentially reducing bias and improving efficiency. However, AI struggles with nuanced judgment and may reinforce biases in training data. ⚖️ Ethical concerns. AI-assisted scientific workflows pose risks, such as bias in hypothesis generation, lack of transparency in automated experiments, and potential reinforcement of dominant research paradigms while neglecting novel ideas. There are also concerns about the overreliance on AI for critical scientific tasks, potentially compromising research integrity and human oversight.