Understanding Technological Evolution

Watch credit card payment in 1980s. Click-clack imprinter. Metal merchant plate. Raised card numbers. Carbon paper form. Slide mechanism over. Physical impression made. One transaction: 3-5 minutes. Your card details on paper. Multiple copies. Merchant keeps one. You keep one. Stored in drawers. This was "secure payment infrastructure." Most of the world lived through this. Manual imprinters. Carbon copies. Phone authorizations. The evolution: • 1950s-1980s: Click-clack machines • 1980s-2000s: Magnetic stripe, electronic terminals • 2000s-2015: Chip cards, EMV standards • 2015-2020: Contactless, NFC 2020+: QR codes, mobile wallets Each generation built on what existed before. Some countries migrated layer by layer. Each transition constrained by protecting previous investment. Others leapfrogged. Built QR-based systems without being bound by card rails. Germany still prefers cash because decades of infrastructure created habits. China built Alipay and WeChat Pay on QR codes when card penetration was low. India built UPI the same way. QR codes. Mobile-first. Instant settlement. Different starting points. Same insight: design for what payments should be, not what they used to be. Which brings me to yesterday. I posted about cashless payments not working at India's AI Impact Summit. Mixed reactions. Dr. Martha Boeckenfeld reminded me Germany still runs on 50% cash. Others pointed out how far India has come. They're right. Yesterday's failure stung because we've come so far we expect it everywhere now. From click-clack taking 5 minutes to QR code taking 5 seconds. That expectation? That's actually privilege. We forget payments used to sound like this. And forgetting how far we've come is progress. Did you ever make a payment using one of these? What do you remember about it? Or are you young enough to have never seen one?

Payments have evolved from paper and plastic to APIs and orchestration - giving rise to a new breed of players that simplify the complexity and connect the dots behind the scenes. Here's how we got here. 𝟭. 𝗜𝗻 𝘁𝗵𝗲 𝗽𝗿𝗲-𝟭𝟵𝟵𝟬𝘀 𝗲𝗿𝗮, banks owned the entire payments value chain -acquiring, processing, settlement. Merchant onboarding was complex, and domestic clearing systems ruled. 𝟮. 𝗧𝗵𝗲 𝗿𝗶𝘀𝗲 𝗼𝗳 𝗲-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 in the late 1990s changed everything. Players like PayPal and Authorize made online payments possible, while banks began exiting the acquiring space or partnering with processors to keep up with demand. 𝟯. 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝟮𝟬𝟬𝟬 𝗮𝗻𝗱 𝟮𝟬𝟭𝟬, specialized gateways and regional wallets began to scale, offering merchants greater flexibility and control. The launch of SEPA in Europe marked a push toward payment harmonization, while non-bank players started building infrastructure that bypassed traditional acquiring models altogether. 𝟰. 𝗧𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝘁𝗼 𝗔𝗣𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 transformed payments from siloed systems into modular, developer-friendly tools. Merchant onboarding became faster, integrations simpler, and innovation more scalable. Open Banking regulations enabled direct access to bank data, while new credit models redefined consumer behavior. Payments evolved into a flexible, programmable layer of the digital economy. 𝟱. 𝗧𝗼𝗱𝗮𝘆, we’re in the age of seamless integration. Payments are embedded in everything - from ride-hailing apps to SuperApps. Real-time rails like SEPA Instant, UPI and PIX are live. CBDCs are in pilot. However, as payment ecosystems grow more fragmented - with new methods, regional schemes, compliance layers, and fraud risks -complexity has become a major bottleneck for merchants, fintechs, and even banks. Integrating multiple providers, maintaining uptime across systems, and ensuring regulatory compliance isn't just costly - it's unsustainable without the right foundation. This is where a new breed of infrastructure players like 𝗔𝗸𝘂𝗿𝗮𝘁𝗲𝗰𝗼 fit in - offering the tools to simplify complexity and still retain control. • 𝗪𝗵𝗶𝘁𝗲-𝗹𝗮𝗯𝗲𝗹 𝗽𝗮𝘆𝗺𝗲𝗻𝘁 𝗴𝗮𝘁𝗲𝘄𝗮𝘆𝘀 let banks, PSPs, and fintechs launch their own branded platforms fast - without building from scratch. • 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 enables merchants to route transactions dynamically across multiple acquirers, reducing costs and failed payments while improving UX. • 𝗕𝗮𝗻𝗸𝘀 can embed API-driven acquiring services into their offerings without the burden of a full-scale tech overhaul. In a world where growth brings fragmentation, the real challenge isn’t enabling payments - it’s managing them. The advantage will lie with infrastructure that can unify complexity, adapt in real time, and scale across borders without adding friction. Opinions: my own, Graphic source: Akurateco Payment Hub 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

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.

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.

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.

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.

Last week, a customer said something that stopped me in my tracks: “Our data is what makes us unique. If we share it with an AI model, it may play against us.” This customer recognizes the transformative power of AI. They understand that their data holds the key to unlocking that potential. But they also see risks alongside the opportunities—and those risks can’t be ignored. The truth is, technology is advancing faster than many businesses feel ready to adopt it. Bridging that gap between innovation and trust will be critical for unlocking AI’s full potential. So, how do we do that? It comes down understanding, acknowledging and addressing the barriers to AI adoption facing SMBs today: 1. Inflated expectations Companies are promised that AI will revolutionize their business. But when they adopt new AI tools, the reality falls short. Many use cases feel novel, not necessary. And that leads to low repeat usage and high skepticism. For scaling companies with limited resources and big ambitions, AI needs to deliver real value – not just hype. 2. Complex setups Many AI solutions are too complex, requiring armies of consultants to build and train custom tools. That might be ok if you’re a large enterprise. But for everyone else it’s a barrier to getting started, let alone driving adoption. SMBs need AI that works out of the box and integrates seamlessly into the flow of work – from the start. 3. Data privacy concerns Remember the quote I shared earlier? SMBs worry their proprietary data could be exposed and even used against them by competitors. Sharing data with AI tools feels too risky (especially tools that rely on third-party platforms). And that’s a barrier to usage. AI adoption starts with trust, and SMBs need absolute confidence that their data is secure – no exceptions. If 2024 was the year when SMBs saw AI’s potential from afar, 2025 will be the year when they unlock that potential for themselves. That starts by tackling barriers to AI adoption with products that provide immediate value, not inflated hype. Products that offer simplicity, not complexity (or consultants!). Products with security that’s rigorous, not risky. That’s what we’re building at HubSpot, and I’m excited to see what scaling companies do with the full potential of AI at their fingertips this year!

AI adoption in enterprises rarely follows a straight line. You can build a capable agent that solves a real problem and still find no one using it. One extra click from the usual process can become an inhibitor. A new window, and your DAU/WAU/MAU can tank. Adoption isn’t just about rolling out a tool; it’s about reshaping ingrained habits. Teams grow so comfortable with existing workflows that AI tools can initially feel like a liability rather than a productivity enhancer. The journey moves through three stages: adoption, adaptation, and transformation. Strategy often starts with the end state (transformation), but execution must begin with the first step: adoption. Each stage requires building trust, lowering friction, and proving value in small, tangible increments. Without that, even the most well-designed AI solutions risk becoming "shelfware". AI isn’t a solo game. It’s a team sport. One weak link, one reluctant user, can cause the whole purpose to fall flat. Success depends not just on technology but on shared conviction. Real transformation happens when every click, every process, and every team member feels like AI isn’t an extra step but the obvious next one. #ExperienceFromTheField #WrittenByHuman

Are we approaching the end of traditional payment rails, or just entering a new phase of evolution? Stablecoins seemingly landed the MVP award at this years Money 20/20 anyway.👇 For years, the foundations of payments have been remarkably stable. Card schemes, clearing houses and correspondent banks have underpinned everything from high street transactions to cross border trade. These systems are deeply embedded, heavily regulated and broadly trusted. But they are also ageing. As tokenised money and stablecoins move from concept to implementation, the payments conversation is shifting. What once sounded like fringe innovation is now being tested at scale by central banks, financial institutions and private players alike. A few themes are worth watching closely: ➡️ New rails, new rules. Stablecoins can settle in real time and reduce reliance on multiple intermediaries. This opens the door to faster, cheaper and programmable payments, particularly in markets where existing infrastructure is fragmented or inefficient. ➡️ Resilience versus agility. Traditional payment systems are proven, robust and grounded in legal certainty. New approaches may offer speed and flexibility, but they must prove their reliability and ability to withstand pressure at scale. ➡️ Interoperability is key. For tokenised payments to reach maturity, they will need to connect seamlessly with the systems already in place. Until that happens, hybrid models are more likely than full replacement. ➡️ Regulation will set the direction. The role of public institutions through policy, oversight and possibly direct issuance will determine how far private stable coin models can develop. Trust in money depends on more than just technology. ➡️ Different markets, different needs. The case for change in wholesale markets such as securities settlement or interbank transfers is not the same as in consumer payments. Innovation may follow parallel but distinct tracks. So the question may not be whether we are abandoning old rails, but how and where the next generation of infrastructure is being built, and who gets to shape it. The future of money is about more than just technology. It raises fundamental questions about who governs our financial systems, who earns our trust and who benefits from change. Money 20/20 last week truly showed me just how far the journey is already under way. What comes next will depend on how well we navigate the trade-offs between speed, safety and control.