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

In 1972, a woman in Cambridge, England, figured out how to make computers understand what we’re actually looking for. Her name was Karen Spärck Jones. https://lnkd.in/g8tdZ-cQ At the time, searching through documents meant reading titles, checking indexes, or hoping you remembered the right keywords. It was slow, manual work. Karen was working with punch cards and early computers, and she realized something simple but powerful: common words like “the,” “and,” or “of” show up everywhere and don’t help you find anything specific. A rare word, on the other hand, is much more useful. She created a mathematical formula that weighed how important a word was in a particular document against how common it was across the entire collection. She called it term frequency-inverse document frequency — TF-IDF. It let a machine figure out relevance without actually understanding the meaning of the words. It was a quiet paper in a niche academic journal. Most people in computing at the time thought language processing was a librarian’s problem, not serious science. Mainframe computers were expensive and mostly used for military calculations, banking, and census data. Karen had to wait for the engineers and physicists to finish their work before she could run her experiments late at night on the university’s big Titan computer. She fed in stacks of punch cards, dealt with jammed readers, and checked everything by hand. She didn’t have a flashy lab or big funding. She just kept working. Decades later, when the internet exploded with billions of pages, search engines hit a wall. Early directories relied on humans manually categorizing everything. It couldn’t scale. Engineers digging through old research found Karen’s 1972 paper. They took her math, scaled it up, and built it into the core of how modern search works. Google, Bing, academic databases, even the search function in your email — they all use some version of what she created. You type a question. The system filters millions of documents in a fraction of a second and gives you what you need. That filtering logic traces straight back to her. Karen stayed at Cambridge. She taught, mentored other women in computing, and kept pushing the field forward until she retired in 2002. She died in 2007. She never got rich. She never became a household name. The giant tech companies that built empires on search rarely mentioned her. But every time you type something into a search bar and actually get a useful answer, you’re using Karen Spärck Jones’s thinking. She didn’t build the internet. She just taught machines how to listen better.

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

I just discovered why 90% of proptech sales fail, and it has nothing to do with the product's features. It's because founders don't understand how real estate developers actually make money. Let me show you the secret math that drives every decision they make. I was catching up with a proptech founder last week. His client, a GP, passed on software that would cost him $500/month. "They said it's too expensive!" he told me, frustrated. Then I showed him the math through the GP's eyes: $500/month = $6k/year = $120k hit to exit value (at a 5% cap rate) With his 20% promote, that's $24k straight out of his pocket. But here's where it gets interesting: Most vendors think real estate is about NOI. It's not. It's about the waterfall. Here's how it actually works: First, debt gets paid. Then, LPs get their principal back + preferred return (usually 8%). Only THEN does the GP get their promote (typically 20% of remaining profits). I used to tell founders: "Pitch the NOI increase!" Now I say: "Show them how to get past their pref faster." Different message. 10x the conversion. The promote is everything. It's why a GP will obsess over a $500/month expense but drop $50k on a lobby upgrade without blinking. One adds to NOI (and helps hit the promote). The other is just a cost. Want to sell into real estate? Stop thinking like a SaaS founder. Start thinking like a GP chasing a promote. Here's the framework I teach: • Calculate the NOI impact • Multiply by the exit cap rate • Show how it affects the promote • Watch them lean forward in their chair Example: "Your current vacancies cost you $10k/month in lost NOI. At a 5% cap, that's $2.4M in lost exit value. With your 20% promote, you're leaving $480k on the table." Now you're speaking their language. Most proptech founders think their enemy is the status quo. Wrong. Your enemy is the 8% pref. Every dollar matters. Every timeline matters. Every basis point matters. Because missing that promote doesn't just hurt the deal. It hurts the GP personally. I spent years watching smart operators pass on great solutions. Turns out they weren't cheap. They were doing math that the vendors didn't understand. Now I teach founders to lead with the waterfall. Sales cycles cut in half. The best prospects? Opportunistic developers 2 years from exit. The worst? Core owners collecting management fees. Different math. Different motivations. Different pitch. Stop selling software. Start selling promotes. P.S. If you want to master this (plus 50+ other frameworks for selling into real estate), we cover all of it in our course on 19th May. Join us- link in the comments. But honestly? This waterfall trick alone will transform your sales. Try it tomorrow. Thank me later.

For years, one of the defining challenges in real estate was how slowly the industry adopted technology. In many ways, that lag is what created the opportunity for Fifth Wall in the first place: a massive, critical industry that sat out decades of software adoption and then had to start modernizing all at once. Even today, despite the growth of a real PropTech ecosystem, adoption is still slower and harder than in most other sectors. Historically, I saw that as a bug. A real constraint on innovation. What has changed is AI. Because so many real estate companies never fully embedded legacy enterprise software into their operations, they may now be in a better position to leapfrog directly into AI-native tools, workflows, and operating models. There is often less infrastructure to rip out, fewer entrenched systems to replace, and more room to build from scratch. That changes the equation. What used to look like resistance is starting to look more like flexibility. What used to look like a gap is starting to look more like a blank slate. And that is increasingly shaping how we think about the next wave of opportunity in real estate technology.

In 1998, Google launched a search engine that would forever change how we access information. Google wasn't the first search engine. Back then, giants like Yahoo and AltaVista dominated the digital landscape. But Google, unlike its competitors, understood something intrinsic. The true pain points of searching for information. Even when there is a leader in the market, if you understand a non-obvious but major consumer pain point, and you can build a product that delights — you take over the market, as Google did. The customer simply cannot go back to the ‘old’, when the product experience is far superior. Imagine searching for "pizza near me". In the days before Google, you might end up with a mishmash of pizza delivery services, historical information about pizza, or even an article on the geometry of a perfect pizza slice or even a recipe about pizza. ‘Search’, was merely a dumb listing of words looked up. It lacked any concept of ranking based on context. Now, let's say you typed "best pizza near me” on Google today. With its understanding of location and user intent, it would prioritize highly-rated local pizzerias with positive reviews, catering to your specific need for a delicious, close-by meal. This seemingly simple innovation — understanding the ‘why’ behind the ‘what’ —  transformed online searches from a frustrating hunt to a seamless discovery process. Google wasn't about being first; it was about being better. From incorporating voice search to offering real-time translation, it continues to adapt and anticipate our ever-changing information needs. Google Search is a game-changer when it comes to the power of user-centric design. It’s a reminder that the most revolutionary ideas sometimes lie in solving the simplest problems, like page ranking algorithms. Almost any student of Computer Science knows how to write the basic code for the algorithm. The insight is in applying it to a specific problem and user experience of the outcome. We take the power of search for granted today, but it has really been an incredible journey of information access we've been handed. As Search continues to evolve, what exciting possibilities lie ahead? Future of search will be transformed by AI. Perhaps your simple voice command “Pizza”, is enough to predict what pizza you feel like eating and automatically order it from your preferred vendor to be delivered at your home. One thing is certain, today's search algorithms will look rudimentary within the next decade. Now, a fun question to search: "Can squirrels actually fly?" Video source: Google #Google #innovation #artificialintelligence #technology #future

Search is no longer about ranking. It is about being selected in the answer. During a recent test, an AI tool was asked for the “best platforms to improve customer retention.” The response was simple. A concise summary with a few brands mentioned. No links to compare. No second page to explore. That shift is hard to ignore. The competition is no longer for clicks. It is for inclusion inside the answer itself. For years, teams optimized for keywords, backlinks, and traffic spikes. Now the focus is shifting toward understanding. AI systems do not just crawl pages. They interpret meaning, connect entities, and surface brands they recognize as credible. When messaging is inconsistent, data is fragmented, or expertise is unclear, visibility quietly drops. This is not just an SEO shift. It is a visibility shift. Clarity outperforms volume. Structure outperforms density. And clearly expressed expertise becomes a signal machines can trust. This week’s newsletter breaks down what AI search really means, why entity authority is gaining importance, and what teams need to change now. For those thinking about how to be part of the answer, not left out of it, this is worth the read.

India’s public transport story is far bigger than metro stations and bullet train headlines. Every morning, before stock markets open and office towers light up, millions of people are already moving through buses, local trains, shared autos, and metro lines that quietly keep the country alive. Around 85 million public transport journeys happen daily in India, making it one of the largest mobility networks in the world. What stands out is how mobility itself has become a signal of economic energy. Maharashtra alone moves nearly 12.5 million passengers every day. Uttar Pradesh carries more than 8 million. Delhi, despite its small geographical size, handles close to 7.8 million daily riders. West Bengal, Tamil Nadu, Karnataka, Bihar, each of them tells the same story. Growth is impossible without movement. Public transport is not just about commuting anymore. It determines how quickly workers reach jobs, how students access education, how businesses expand, and how cities avoid collapsing under congestion. A reliable bus route can change employment access for an entire district. A metro corridor can reshape real estate, productivity, and local commerce within a few years. While the world often associates development with highways and private cars, India’s real strength still lies in moving massive populations efficiently at low cost. That is the hidden engine behind urban growth, rising consumption, and expanding economic participation. The countries that dominate the next decade may not necessarily be the ones with the most luxury vehicles, but the ones capable of moving the highest number of people seamlessly every single day. And India is already proving what scale truly looks like. 🇮🇳

Think some of the technologies we enjoy today are purely modern inventions? History has a way of surprising us. Take scuba diving, for instance. While it’s easy to associate it with sleek wetsuits and oxygen tanks, the concept dates back thousands of years to the Assyrian Empire. ↳ The Assyrian Inflatable Goatskin Bag Depicted on a 9th-century BCE tablet housed in the British Museum, Assyrian soldiers were shown crossing rivers using inflatable goatskin bags. → These ingenious devices acted as early life preservers, offering buoyancy and even a source of air, much like a primitive snorkel. → Soldiers used this technique to remain undetected during military campaigns, blending technological ingenuity with strategic brilliance. But the Assyrians weren’t alone in creating early versions of “modern” technologies. ↳ Ancient Egyptian Prosthetics (3,000 BCE) The Egyptians crafted wooden toes and other prosthetic devices, blending form and function to aid amputees. → These artifacts not only showcased advanced craftsmanship but also highlighted the Egyptians’ deep understanding of anatomy and empathy. ↳ Babylonian Astronomical Calculations (1,200 BCE) The Babylonians used clay tablets to record the movements of celestial bodies with astonishing precision. → Their innovations formed the foundation of modern astronomy and mathematics, influencing civilizations across millennia. ↳ The Greek Steam Engine (1st Century BCE) Hero of Alexandria designed the aeolipile, a steam-powered device, centuries before the Industrial Revolution. → While initially a novelty, it demonstrated principles that would later drive the modern age of machinery. What Can We Learn From These Ancient Innovations? The ingenuity of early civilizations reminds us of humanity’s boundless creativity. Despite lacking advanced tools, these societies developed solutions that rival—and sometimes predate—our modern technologies. It’s humbling to consider that many of the innovations we take for granted were born out of necessity and imagination thousands of years ago. The lesson? Progress isn’t always about reinventing the wheel—it’s about building on the creativity of those who came before us. Which ancient innovation inspires you the most? Image: Ingvar Svanberg, Isak Lidström, Folk Life Journal / Jolene Creighton