Understanding Technological Evolution

AI is rapidly moving from passive text generators to active decision-makers. To understand where things are headed, it’s important to trace the stages of this evolution. 1. 𝗟𝗟𝗠𝘀: 𝗧𝗵𝗲 𝗘𝗿𝗮 𝗼𝗳 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗙𝗹𝘂𝗲𝗻𝗰𝘆 Large Language Models (LLMs) like GPT-3 and GPT-4 excel at generating human-like text by predicting the next word in a sequence. They can produce coherent and contextually appropriate responses—but their capabilities end there. They don’t retain memory, they don’t take actions, and they don’t understand goals. They are reactive, not proactive. 2. 𝗥𝗔𝗚: 𝗧𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗖𝗼𝗻𝘁𝗲𝘅𝘁-𝗔𝘄𝗮𝗿𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Retrieval-Augmented Generation (RAG) brought a major upgrade by integrating LLMs with external knowledge sources like vector databases or document stores. Now the model could retrieve relevant context and generate more accurate and personalized responses based on that information. This stage introduced the idea of 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗮𝗰𝗰𝗲𝘀𝘀, but still required orchestration. The system didn’t plan or act—it responded with more relevance. 3. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜: 𝗧𝗼𝘄𝗮𝗿𝗱 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Agentic AI is a fundamentally different paradigm. Here, systems are built to perceive, reason, and act toward goals—often without constant human prompting. An Agentic system includes: • 𝗠𝗲𝗺𝗼𝗿𝘆: to retain and recall information over time. • 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴: to decide what actions to take and in what order. • 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲: to interact with APIs, databases, code, or software systems. • 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆: to loop through perception, decision, and action—iteratively improving performance.    Instead of a single model generating content, we now orchestrate 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝘀, each responsible for specific tasks, coordinated by a central controller or planner. This is the architecture behind emerging use cases like autonomous coding assistants, intelligent workflow bots, and AI co-pilots that can operate entire systems. 𝗧𝗵𝗲 𝗦𝗵𝗶𝗳𝘁 𝗶𝗻 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 We’re no longer designing prompts. We’re designing 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 capable of interacting with the real world. This evolution—LLM → RAG → Agentic AI—marks the transition from 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 to 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲.

🌳 Design Patterns For Building Trust. With practical guidelines for designers on how to make products — AI and non-AI — more trustworthy, reliable and honest. In the noisy and polluted world today, trust doesn’t come for free. It doesn’t emerge by default. It must be earned and meticulously preserved — by being reliable, accountable and treating customers with respect. This holds true for people but it also for software. According to Anyi Sun, there are 5 psychological foundations of user trust: 1. Reliability 🔰 The degree to which the product consistently behaves as expected. It's a sense that that the product is dependable — based on a track record of past actions. Reliability comes from promising what you do, and doing what you promised. 2. Technical competence ⚡ Perceived intelligence, sophistication and capability of the product. It's user's belief that the product can successfully perform what they are being trusted to do. It's about trusting product's capability. 3. Understandability 🧠 The extent to which users feel they can understand how the system works or why it made a certain decision. The product must be able to articulate how a decision came along, with references to fragments that underpin a decision. 4. Faith and Care 🌱 Emotional, almost "blind trust" in the product, especially when users don't understand the underlying logic. It's a belief that the trusted party actually cares about the positive outcome for you, and intends to do good. 5. Personal attachment 🌳 A sense of rapport, connection or emotional engagement with the product. Typically it emerges when a user feels that they get meaningful value from the product, and from interactions with people supporting it. Personally, I would also add the value of repeated positive experiences that build confidence in the quality of the product, and hence its reliability. --- With AI products, hitting all these psychological foundations is extremely hard. Surely some people trust AI almost instinctively, others are more critical. But people's attitude often changes dramatically once they realized that they've made severe mistakes because of AI. Recovering from it is very hard. We can help with some design patterns: 1. Avoid "Ask me anything" → push for scoping and constraints 2. Slow down users in prompting → request specific details 3. Present multiple viewpoints, explain that experts disagree 4. Allow users to manage “memory”, profiles personalization 5. Highlight what is AI-generated and what isn't (AI disclosure) 6. Allow users to override AI-generated suggestions manually 7. Allow users to tweak AI output and refine it for their needs 8. Adapt AI's tone depending on the severity of user's task Trust is why people stay or leave. It builds long-term loyalty and helps users overcome hesitation. But it must be designed and retained — across all psychological foundations and with thoughtful UX work. I think designers will be quite busy for years to come. #ux #design

NEW in Foreign Affairs Magazine: Kurt Campbell and I argue that any serious China strategy must begin with an old truth: “Quantity is a quality all its own.” Scale matters. China has it. We can only match it through a new grand strategy of allied scale. We hang together—or separately. 🔗 Read here: https://lnkd.in/dxiRnNZN 🔸 Eight highlights from the article: 1️⃣ UNDERESTIMATING CHINA: China is slowing, aging, and indebted. But economic challenges don’t neatly translate into strategic disadvantage—especially not on the metrics and timeframes that matter in great power competition. 2️⃣ SCALE AND GREAT POWER DECLINE: The UK had a first-mover advantage. But once larger countries industrialized—Germany, the US, Russia—they outscaled it. From 1870–1910, Britain’s manufacturing share halved. 3️⃣ AMERICAN SCALE: American scale built Pax Americana. Hitler called the US a “giant state with unimaginable productive capacities." Yamamoto said Japan could hold out 6 months—no more. Italian leaders feared US “stamina.” 4️⃣ CHINA OUTSCALES THE US: That scale now belongs to China. China has: • 2× US manufacturing, 4× by 2030 (UN) • 2× US power generation • 3× car production • 13× steel output • 20× cement • 200× shipbuilding Global share: • 50% of chemicals, ships • 67% of EVs • 75% of batteries • 80% of drones • 90% of solar panels, rare earths It’s seizing the future: • 50% of industrial robots (7× US) • Leading in 4th-gen nuclear • 100+ new reactors planned • Top in patents, pubs Military scale: • 1.5× US naval vessels by 2030 (PRC 435 to US 300) • Leads in hypersonics, quantum comms • Indigenizing jet engines • Building 100 4th-gen fighters/year 5️⃣ ASSESSING CHINA: China is slowing—but also formidable. In GDP, China is 25% larger adjusted for PPP ($30T vs $24T). It is aging, but the under-15 share rose (2010–2020 “echo boom”) and the dependency ratio worsens post-2050. Debt is high, but similar to US. Housing is a bust, but credit is redirected to industry. US firms lead on profits, but Chinese firms pursue market share at a loss to win the long game. 6️⃣ BUT US ALLIES OUTSCALE CHINA: Today, the US, EU, Japan, Korea, India, Australia, Canada, Mexico, and NZ outscale China: • 3× China’s nominal GDP • 2× PPP GDP, defense spending • 1.5× manufacturing share • More patents, citations • Top trading partner of most countries 7️⃣ UNLOCKING ALLIED SCALE: Allies scale outclasses China—but only in theory. Making this real is the central task of US statecraft. Alliances must become platforms for building capacity. Japan & Korea build US ships, Taiwan makes US chips, US shares defense tech with allies, allies erect a shared wall against China’s overcapacity, etc. 8️⃣ THE WAY FORWARD: We need to go beyond even Biden’s alliance-first approach. We must avoid go-it-alone instincts and act on what Beijing already knows: 👉 Our alliances are our decisive asymmetric advantage.

AI didn’t happen overnight, and it’s not one single concept. It’s the result of decades of progress - each breakthrough paving the way for the next. Here’s how the key building blocks fit together in the evolution of AI: 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 (𝗔𝗜) – technology that can analyse information, reason, and make context-based decisions without needing explicit instructions for every step. It’s the foundation for everything that followed. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗠𝗟)  – a branch of AI where systems learn from data instead of following fixed rules. They identify patterns and relationships in large datasets and adjust their behaviour accordingly. 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗡𝗡)  – a type of ML model inspired by the human brain. They’re especially good at recognising complex patterns, such as faces in photos, words in speech, or meaning in text. 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗗𝗟)  – an advanced form of neural networks with many layers, trained on massive datasets. This made AI accurate enough for real-world use in language translation, image recognition, and voice assistants. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗜  – the most common application of ML and DL today. It analyses historical data to predict what’s likely to happen next — from credit risk and demand forecasting to customer churn or fraud detection. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 (𝗚𝗲𝗻𝗔𝗜)  – a newer approach where AI doesn’t just analyse data but creates new content — writing text, generating images, coding, or composing music — based on what it has learned. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀  – autonomous applications that can make decisions and take actions on our behalf. They plan tasks, use other tools or systems, and complete goals with little or no human involvement. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 – a more advanced stage where multiple autonomous agents work together, share context, and make coordinated decisions to achieve broader goals. They don’t just execute tasks — they plan, adapt, and collaborate while remaining under human oversight. In reality, AI in its current form is really about extending human intelligence — and doing it at scale. Opinions: my own, Graphic sources: Gina Acosta Gutiérrez, Infinity Learning Subscribe to my newsletter: https://lnkd.in/dkqhnxdg

The Hoysaleswara Temple in Halebidu, Karnataka, stands as a testament to India's rich architectural and engineering heritage. Among its many intricate carvings is a depiction of Masana Bhairava, a fierce form of Lord Shiva, holding what appears to be an advanced mechanical device. This sculpture has sparked discussions about the technological prowess of ancient Indian artisans. The device in question resembles a planetary gear system, characterized by an outer gear with 32 teeth and an inner gear with 16 teeth—a precise 2:1 ratio. Such mechanisms are fundamental in modern engineering, used in applications ranging from automobile transmissions to sophisticated machinery. The presence of this depiction in a centuries-old temple raises intriguing questions about the depth of mechanical knowledge possessed by our ancestors. Key Insights: 1. Advanced Understanding of Mechanics: The accurate representation of a planetary gear system suggests that ancient Indian craftsmen had a sophisticated grasp of mechanical principles. This challenges the conventional narrative that such knowledge was absent in ancient times. 2. Integration of Art and Science: The fusion of intricate artistry with precise mechanical representation indicates a holistic approach to knowledge, where art and science were not seen as separate domains but as interconnected disciplines. 3. Preservation of Knowledge: The detailed carvings serve as a medium to transmit complex ideas, ensuring that such knowledge was preserved and communicated across generations. This discovery not only highlights the ingenuity of ancient Indian artisans but also underscores the importance of re-examining historical artifacts with a fresh perspective. It prompts us to appreciate the advanced understanding embedded in our cultural heritage and encourages further exploration into the technological achievements of ancient civilizations. As we marvel at the Hoysaleswara Temple's architectural splendor, let us also acknowledge and celebrate the profound scientific insights it encapsulates. This serves as a powerful reminder of the rich legacy of innovation and knowledge that forms the foundation of our present and future advancements. #AncientIndia #EngineeringMarvels #CulturalHeritage #PlanetaryGears #HoysaleswaraTemple #Innovation

AI adoption. Nothing new here — yet we keep falling into the same trap We’ve seen it with every major technology rollout in history: organizations focus on technology first, assuming that if the tool is powerful, adoption will naturally follow. But the data show the same pattern repeating— and it’s happening again with AI. AI is improving fast, yet adoption seems to stall. Nothing new here. Even positive changes disrupt routines, and many employees stick with familiar processes because they fear complications, slowdowns, or automation replacing their role. Deloitte’s recent TrustID Index tells a familiar story: Trust in company-provided generative AI fell 31% between May and July 2025. Interestingly the index also shows 43% of employees admit to using unapproved AI tools, often due to lower trust in official systems. In other words there is a basis of interest in the new technology that companies could leverage if they just would be smarter in their AI deployment. Trust rises when AI is: . Integrated into workflows . User-friendly . Supported with training . Shown in real-world examples . Recommended by peers Nothing groundbreaking here, but many companies simply don’t do it enough. Employees don’t inherently distrust AI — they distrust how their company implements and supports it. Access alone won’t guarantee adoption. To succeed, leaders must prioritize trust, clear communication, and human-centered design, not just the technology itself.

We’re heading towards a world where language is the way we interface with technology. Over the past few decades, GUIs and touchscreens have served as our primary gateways to digital systems. Today, the rapid advancement in natural language processing and AI signals a paradigm shift. Our words––spoken or written––are becoming the direct inputs that drive production. The result is more users being able to pilot technology in a way that serves them. More people can build websites, code, or create 3D models. In embracing language as the primary interface, technology becomes more aligned with the subtleties of human thought. AI systems become more positioned to amplify our capabilities by understanding the nuance behind every phrase and responding with relevant intelligence. Every conversation and piece of writing transforms into a dynamic interaction that gathers context, anticipates needs, and offers tailored suggestions; paving the way for a group of AI agents to support all of us, whatever we’re working on. 

Saw literally 100's of posts saying: USA : ChatGPT China : DeepSeek India : Course on how to use them I couldn't resist but ask a simple question to all these people: When was the last time you built something? An app? A tool? Even a simple automation script? Or is your biggest contribution to tech is such posts? Because here’s what’s actually happening in India: ✅ AI & LLMs – India is home to Bhashini, a government-led multilingual AI initiative, and Sarvam AI, developing indigenous LLMs tailored for Indian languages. ✅ Semiconductors & Chips – Companies like Vedanta, Tata, and ISRO are investing heavily in semiconductor fabs, reducing dependency on global supply chains. ✅ Space Tech – ISRO’s Chandrayaan-3, Aditya-L1, and the upcoming Gaganyaan mission are pioneering space exploration on a budget that puts Hollywood sci-fi movies to shame. ✅ Fintech Revolution – India leads in UPI, Aadhaar-enabled banking, and RBI-backed digital currency, with real-time payments surpassing the USA, China, and EU combined. ✅ 5G & Telecom – Jio and Airtel are deploying indigenous 5G solutions, positioning India at the forefront of telecom innovation. ✅ EV & Clean Energy – India is pushing hard in EV manufacturing, solar energy, and green hydrogen with companies like Ola Electric, Tata, and Adani leading the way. ✅ Startups & Deep Tech – India has 100+ unicorns, with cutting-edge work happening in robotics, blockchain, and AI-driven healthcare. Meanwhile, in the USA and China, innovation continues in AI chip design, quantum computing, self-driving tech, and advanced robotics. And guess what? India has the talent to be right there, but only if more people build instead of tweet. Innovation doesn’t happen in comment sections or such posts—it happens when you do something. So, the next time you feel like typing one of these lazy takes, ask yourself: "Am I just talking about innovation, or am I actually creating it?" #BuildSomething #Innovation #Tech #IndiaInTech 🚀

At PwC, we've learned that the biggest barrier to scaling enterprise AI isn't model capability: it's trust. Here's how we think about that problem. Every new technology faces the same deadlock: you don't use it because you don't trust it, and you don't trust it because you don't use it. The way out is usually a trust proxy, a visible marker that tells people it's safe to change their behavior. The SSL padlock is the classic example. Ecommerce was technically possible in the 1990s, but adoption stalled because typing a credit card into a browser felt reckless. The padlock didn't create security, the encryption was already there. It made security visible. Enterprise AI faces the same issue. The models work. Real solutions exist. But capability is compounding faster than confidence. You see it in cautious adoption: professionals double-checking outputs the system got right. Not because the models aren't good enough, but because there's no structured way to show they've been rigorously evaluated by people who know what good looks like. These aren't capability problems. They're trust infrastructure problems. That's what we built Evaluation Navigator and the Human Alignment Center to address. 📊 Evaluation Navigator gives AI teams a consistent, repeatable way to evaluate solutions across the development lifecycle, with shared guidance and standardized reporting. By embedding evaluation directly into developer workflows through an SDK, trust markers are built into the solution as it's constructed, not stapled on before deployment. 🧐 The Human Alignment Center adds structured expert review at scale. Automated metrics can assess technical correctness, but in professional services the real question is whether the output reflects experienced professional judgment. The Human Alignment Center translates that judgment into dashboards and audit trails that governance leaders can actually act on. The padlock made invisible security visible. Evaluation infrastructure does the same for AI. Adoption is a trailing indicator of trust, so as evaluation becomes visible and accessible, adoption follows.

GenAI adoption is all about people, not about tools. Pharma giant Novo Nordisk offers a great case study of working out what supports useful uptake of AI across a large organization. A case study in MIT Sloan Management Review uncovers a range of useful lessons. Here are some of the most interesting. 🚀 Recognize a mid-cycle drop as normal. Novo Nordisk grew Copilot use from a few hundred to 20,000 users in just over a year, with 23% becoming frequent users within one month. However, by month three or four, 15% of early adopters dropped off and average time saved per week declined. Recognizing this dip as natural helped avoid panic and kept the focus on re-engagement strategies rather than getting staff to try tools for the first time. 🛠 Deliver function-specific training through champion networks. Generic AI onboarding failed to meet the needs of specialized roles. Novo Nordisk succeeded by creating domain-specific training, leveraging internal champions to contextualize AI use, and allowing teams to shape guidance based on their actual work. This addressed “AI shaming” and bridged confidence gaps across functions. 🤝 Use internal champions to overcome cultural resistance. Skepticism wasn’t solved by policy, it was shifted by influence. Novo Nordisk identified trusted, high-status employees to openly adopt and advocate for AI tools. Their visible endorsement encouraged hesitant peers to try AI without fear of judgment or failure. 📈 Treat adoption as a change process, not a tech rollout. Rather than pushing a one-time launch, Novo Nordisk framed GenAI as a long-term transformation. This meant investing in ongoing communication, support structures, and iterative learning. The approach acknowledged that adoption would ebb and flow, and prepared the organization to adapt accordingly. 🎯 Emphasize strategic value over time saved. Though average users saved about 2 hours per week, the most meaningful wins came from higher-quality work—more strategic thinking, clearer writing, and better planning. By highlighting these human-centric gains, Novo Nordisk built a stronger case for AI’s workplace relevance beyond mere productivity. 📊 Use employee data to shape the deployment strategy. Over 3,000 employee surveys and interviews helped Novo Nordisk spot where and why adoption lagged. This feedback guided real-time adjustments—like where to invest in new use cases, where to scale back, and how to tailor messaging. It also surfaced which functions became tool-reliant versus those needing more support.