AI Predictions for 2025 (Part 2)

General – 1

Ben Evans in his presentation:

2025 predictions via Perplexity:

  1. AI-native applications will revolutionize technology, with breakthrough companies reaching $50M in annual recurring revenue and transforming how businesses leverage intelligent systems.
  2. Autonomous AI agents will become dramatically more sophisticated, capable of executing complex multi-step tasks with minimal human intervention and potentially beginning to design and improve other AI systems.
  3. The global economic impact of AI will be staggering, with projections indicating contributions of $200-$340 billion annually in sectors like banking, finance, and technology.
  4. Major AI model providers like OpenAI, Anthropic, and Meta will continue aggressive investments, with more restrictive licensing and a shift towards building practical, industry-specific applications.
  5. Quantum AI and advanced computational technologies will push the boundaries of machine learning, enabling unprecedented problem-solving capabilities across scientific and industrial domains.
  6. Content generation will see explosive growth, with AI potentially producing entire research papers, generating high-quality video content, and creating increasingly nuanced creative works.
  7. Cybersecurity will become both more vulnerable and more resilient, with AI simultaneously presenting significant threats and offering advanced protective technologies.
  8. The regulatory landscape for AI will remain complex, with governments struggling to create comprehensive frameworks that balance innovation and potential risks.
  9. Workforce dynamics will dramatically transform, with AI augmenting human capabilities and creating new job categories while rendering some traditional roles obsolete.
  10. Decentralized AI systems will emerge, focusing on enhanced authentication, predictive analytics, and more personalized technological interactions across multiple platforms.

FT asks (and answers) 4 questions:

  • Will AI development hit a wall? The most promising developments look like those coming from models that carry out a series of steps before returning an answer, allowing them to query and refine their first responses to deliver more “reasoned” results. It is debatable whether this is really comparable to human reasoning, but systems such as OpenAI’s o3 still look like the most interesting advance since the emergence of AI chatbots.
  • Will AI’s ‘killer app’ emerge? [2025] is likely to bring the first demonstrations of apps that can intervene more directly: absorbing all your digital information and learning from your actions so that they can act as virtual memory banks or take over entire aspects of your life…Instead of true killer apps for AI, this means we will be left in the “AI in everything” world that technology users have already become accustomed to
  • Will Nvidia’s GPUs still rule the tech world? Even as its market share starts to erode, though, Nvidia’s software still represents a considerable moat for its business, and by the end of the year it should be on the verge of another important new product cycle.
  • Will the stock market’s AI boom continue?With Big Tech in the midst of an AI race that its leaders believe will determine the future shape of their industry, one of the main forces behind the AI capital spending boom will remain in place.

Observer:

  • Agentic A.I. will be “the next giant breakthrough”
  • Test-time compute could be a solution to A.I.’s training data crisis
  • Synthetic data is another promising solution
  • “Large world models” will create 3D A.I. worlds
  • I. search engines will reshape online search

IBM:

  • Agentic AI
  • Inference Time Compute
  • Very Large Models
  • Very Small Models
  • More Advanced Use Cases
  • Near Infinite Memory
  • Human-in-the-Loop Augmentation

Thinks 1464

FT: “Lack of internal deliberation abilities — thinking, in other words — has long been considered one of the main weaknesses of artificial intelligence. The scale of a recent advance in this by ChatGPT creator OpenAI is a point of debate within the scientific community. But it leads many of my expert colleagues and me to believe that there is a chance that we are on the brink of bridging the gap to human-level reasoning…Better reasoning would address two major weaknesses of current AI: poor coherence of answers and the ability to plan and achieve long-term goals. The former is important in scientific uses and the latter is essential to create autonomous agents. Both could enable important applications.”

Narayan Ramachandran: “India’s hope of becoming an upper middle-income country cannot be realized in this environment of statism, because it will fail the cause of deepening prosperity throughout the population. Intricate rule-based regulation must give way to framework regulation…The state has a very important role to play as a standards setter and enabler of fair competition. If the state were to function as an enlightened referee, rather than as umpire, player and promotion board, then individual actors would innovate and contribute much more to an economy that could underpin an inclusive and prosperous country.”

VentureBeat: “At the heart of Microsoft’s strategy is what Lamanna calls the “agent mesh” – an interconnected system where AI agents collaborate to solve complex problems. Rather than operating in isolation, agents can pass tasks, messages, and knowledge seamlessly across the enterprise. Copilot Studio has been associated so far with agents that are triggered via chat, but now Microsoft is emphasizing any kind of actions. Imagine an enterprise where agents collaborate seamlessly: A sales agent triggers an inventory agent to check stock availability, which then notifies a customer service agent to update the client. This architecture includes: autonomous agents that detect events and trigger actions without human oversight, an orchestration layer that coordinates multiple specialized agents, and real-time monitoring tools that provide transparency into agent workflows.”

Nate Silver: “Why did polling still feel so unsatisfying? In a world where the parties are remarkably efficient at corralling voters and competing to a 50-50 split each time, polls aren’t going to provide the certainty we crave. We’d better get used to it: This is now the fourth election in a row in which the popular vote margin was within five points, something that has happened only once before in the country’s history, for six [US] elections between 1876 through 1896. The problems with polls are the same problems that plague politics. Polling has become a mirror that reflects the frustrating, even infuriating, nature of politics in America in 2024. Our politics are messy, and that is not something polls can fix. We’d better get used to that, too.”

Economist: 10 trends to watch in 2025. Among them: “Crunch time for AI: It’s the biggest gamble in business history: more than $1trn is being spent on data centres for artificial intelligence (AI), even though companies are still not sure how to use it and adoption rates are low (though many workers may simply be using it in secret). Will investors lose their nerve, or will AI prove its worth, as “agentic” systems become more capable and AI-developed drugs emerge?”

AI Predictions for 2025 (Part 1)

2024

As we enter a new year, it’s the perfect time to look ahead and make predictions about what lies on the horizon. In this essay, I’ve gathered a range of forecasts on how AI may evolve by 2025, drawing from multiple experts and reliable sources. Though these predictions are speculative, they aim to prompt us to reflect on the future and consider how it might shape the way we think and work.

But before we get to 2025, lets consider the key developments in AI in the past year.

I asked Perplexity for the top 10 AI developments in 2024:

  1. OpenAI’s o1 Model: Advanced reasoning capabilities in mathematics and coding.
  2. Agentic AI: A groundbreaking development offering unprecedented autonomy in decision-making, capable of analyzing complex situations and executing multi-step tasks with minimal human intervention.
  3. AI Search Technologies: Large language models transforming information retrieval, potentially disrupting traditional search engine models.
  4. Google DeepMind’s GenCast: Enhanced weather prediction technology to optimize agriculture and disaster preparedness
  5. Microsoft’s Copilot Vision: AI system designed to enhance productivity by integrating with digital environments.
  6. Advanced AI Video Creation Tools: Innovative platforms like Google’s Veo and OpenAI’s Sora revolutionizing content creation.
  7. Anthropic’s Claude Chatbot: Focusing on AI safety and reliability for critical applications like disaster response.
  8. Conversational AI Advancements: More natural, human-like interactions with significant improvements in understanding complex queries.
  9. Quantum AI: Utilizing quantum computing to enhance AI algorithms and solve complex computational problems.
  10. Generative AI for Hyper-Personalization: AI systems capable of creating highly personalized content, products, and experiences across various sectors.

Forbes:

  • The continuing evolution and advancement of Generative AI chatbots
  • Apple enters the Generative AI arena with Apple Intelligence
  • Head of Google AI wins the Nobel Prize for Chemistry
  • The EU AI act – AI legislation begins to take shape
  • Optimus breaks new ground for humanoid robots

VentureBeat:

  • OpenAI expanded far and wide beyond ChatGPT
  • Open-source AI took off
  • Google’s Gemini series became a serious contender for the best available
  • Agentic AI swept the enterprise

Mint on AI innovations in 2024:

  • OpenAI’s Sora Turbo enhances AI video generation
  • Google updates Veo and Imagen models
  • Microsoft introduces autonomous AI agents
  • Claude AI adds custom writing styles
  • xAI unveils Grok-2 models

Ashu Garg: “For me, the story of 2024 in technology can be summed up in a single number: 1000x. That’s the factor by which the cost of machine intelligence has fallen in just three years – from $60 per million tokens with GPT-3 in 2021 to $0.06 with Meta’s Llama 3.2. To my knowledge, this represents the most rapid democratization of any technological capability in human history. Intelligence, once humanity’s most precious and scarce resource, is becoming ubiquitous, abundant, and essentially free.”

Myra Roldan: “2024 was the year AI got a little glow-up. Multimodal AI stole the spotlight, combining text, audio, and visuals into single models that can do it all, maybe not well all the time but good enough. Then we had the quiet rise of Small Language Models (SLMs) — an attempt at launching little powerhouses that can run on smaller devices with stripped features. And finally, Customizable Generative AI became the must-have trend, as businesses attempted to get it together and started to lean into tailored AI systems.”

A list from appinventiv:

  1. Conversational AI
  2. Predictive Analytics
  3. AI Democratization
  4. Ethical and Explainable AI
  5. Multi-Modal AI
  6. Digital Twins
  7. CoBots
  8. Cybersecurity
  9. Generative AI
  10. Shadow AI
  11. Agentic AI
  12. Retrieval-Augmented Generation (RAG)
  13. Sentimental AI
  14. Quantum AI

Top 10 AI tools:

  1. ChatGPT – 50%
  2. Bing AI – 15%
  3. Google Gemini – 12%
  4. Claude – 8%
  5. MidJourney – 5%
  6. Hugging Face – 4%
  7. Jasper – 2%
  8. Janitor AI – 1.5%
  9. Civitai – 1.3%
  10. Perplexity AI – 1.2%

Simon Willison writes about LLMs in 2024:

  • The GPT-4 barrier was comprehensively broken
  • LLM prices crashed, thanks to competition and increased efficiency
  • Multimodal vision is common, audio and video are starting to emerge
  • Voice and live camera mode are science fiction come to life
  • Prompt driven app generation is a commodity already
  • “Agents” still haven’t really happened yet
  • The rise of inference-scaling “reasoning” models

Thinks 1463

Jakob Uszkoreit: “Biological software is a little bit like computer software in that you have some specification of the behavior that you want, and then you have a compiler that translates that into a piece of computer software that then runs on a computer exhibiting the functions or the functionality that you specify. You specify a piece of a biological program and you compile that, but not with an engineered compiler, because life hasn’t been engineered like computers have been engineered. But with a learned AI compiler, you translate that or compile that into molecules that when inserted into biological systems, organisms, our cells exhibit those functions that you’ve programmed into…A very, very simple example of that are the mRNA COVID vaccines where the program says, “Make this modified viral antigen” and then our cells make that protein. But you could imagine molecules that exhibit far more complex behaviors. And if you want to get a picture of how complex those behaviors could be, just remember that RNA viruses are just that. They’re just an RNA molecule that when entering an organism exhibits incredibly complex behavior such as distributing itself across an organism, distributing itself across the world, doing certain things only in a subset of your cells for a certain period of time, and so on and so forth. And so you can imagine that if we managed to even just design molecules with a teeny tiny fraction of such functionality, of course with the goal not of making people sick, but of making them healthy, it would truly transform medicine.”

WSJ: “You can ask for almost anything, it turns out. You might not get it. But if you do it right, it shouldn’t hurt you. Always have what William Ury—co-author of the negotiation bible “Getting to Yes”—calls a BATNA, or best alternative to a negotiated agreement. In other words, make yourself a solid plan B before you make an ask. If their answer is no, will you walk, focus on another project or start a side hustle? How good is the plan B compared to what you’re asking for? The more alluring the alternative, the more power you have in the negotiation. Think creatively. Do you need a raise, or do you need a more flexible schedule or an unpaid sabbatical? You’re not making demands, you’re starting a conversation.”

Sully Omarr: “One-off agent tasks are some of the least imaginative use cases…rather have an agents automating a 2 minute task 10,000 times over a 15-minute task once.” [via Arnold Kling]

Somak Raychaudhury: “The template we have… go to university at 18, stop learning at 21, stay with it all your life — that can’t happen now…When you come to universities, writing is a core skill that is expected. People often think that if you do sciences, you don’t need to write. That’s not true. In the 21st century, we need problem-solving skills at all levels, not just in social sciences and humanities. We need writing skills, comprehension skills, critical skills. The schools don’t prepare the students for this. There are certain boards like the IB. They have courses to do this. In our core school board system, we don’t encourage critical thinking. Writing has to be underlying every process of education.”

Bloomberg: “[Tim Berners-Lee has] been trying to steer the web back to that free and democratic idea. His answer is a digital wallet, a piece of the internet that stores everything from your medical records to your social media posts, your shopping history to your family photos. But unlike the siloed apps and services we use today, the wallets allow you to control exactly who sees what. Berners-Lee has been working on this radical idea for five years through a startup called Inrupt…For most of the rest of us on the internet that Berners-Lee started, the future is clear. Our personal information will remain scattered across countless databases, increasingly processed by AI systems that serve the interests of large technology conglomerates. It’s not that better alternatives don’t exist, but the companies fashioning our AI futures have too much to lose by giving users control over their digital lives.”

Only Once in Action: NeoMails, SmartBlocks, and AI Twins

1

Overview

In my previous essay, I introduced the Only Once theory, a radical reimagining of marketing, challenging the wasteful status quo where brands repeatedly pay to reacquire their own customers. This reacquisition cycle, responsible for an estimated 50-80% of a brand’s digital advertising budget and totalling to $350-560 billion in annual AdWaste, isn’t just inefficient—it’s a fundamental failure of modern marketing. The solution lies in a powerful trinity of innovations that together enable brands to acquire customers just once, then build lasting, profitable relationships.

At the heart of this transformation is NeoMail, which turns traditional email from a one-way broadcast channel into an interactive “hotline” for continuous customer engagement. By leveraging AMP and CSS technologies, NeoMails are 15-second daily engagement vehicles that create consistent touchpoints that maintain brand presence. They enable in-email actions, from purchases to feedback, without requiring clickthroughs to external sites. Enhanced with Atomic Rewards (Mu) to drive opens and ActionAds for monetisation, NeoMails are more than just links to websites—they become destinations themselves, dynamic microsites delivered directly to the inbox.

The second pillar, SmartBlocks, revolutionises how brands collect and utilise customer data. These interactive containers, seamlessly embedded within emails, enable progressive profiling through natural, rewarding interactions. Instead of interrupting customers with surveys or forms, SmartBlocks gather zero-party data—information voluntarily shared by customers—through engaging elements like polls, quizzes, and preference selectors. Each interaction delivers immediate value while deepening customer understanding, creating a virtuous cycle where better insights lead to more relevant experiences.

The final component, AI Twins, enables true N=1 personalisation by creating individual digital replicas that anticipate each customer’s needs. Starting with Adtech Twins built from public data, evolving to Madtech Twins combining marketing and advertising insights, and ultimately achieving true Customer Twins, this technology enables unprecedented personalisation at scale. Powered by Large Customer Models and Agentic AI, these Twins ensure every interaction feels personally crafted for each customer.

Together, these three innovations create a self-reinforcing ecosystem: NeoMails provide the engagement channel, SmartBlocks gather the necessary data, and AI Twins power the personalisation. This combination solves the fundamental challenges that have forced brands into costly reacquisition:

  • The “No Hotline” problem of poor repeat engagement
  • The “Not for Me” problem of irrelevant non-personalised messaging

For the first time, brands have the technological capabilities to break free from the profit-killing spiral of continuous acquisition. By implementing these innovations, they can transform random customer transactions into predictable recurring revenue, ultimately achieving “profipoly” status—where superior customer understanding and engagement create lasting competitive advantages.

This series will explore how brands can put Only Once into action, detailing practical implementation strategies for each innovation while showcasing their combined power to redefine customer relationships. The future of marketing isn’t in endless acquisition—it’s in building lasting connections to minimise CAC, maximise LTV, and multi-monetise customers through these transformative technologies.

But before we explore these innovations in detail, let’s understand the problem they solve: AdWaste.

2

AdWaste

In my conversations with marketers over the past few years, I always ask one question: “Do you know how much you’re spending on wrong acquisition and reacquisition?” The response is invariably a blank stare, followed by nervous laughter. It’s the elephant in the room no one wants to acknowledge. After all, which CMO wants to admit to their CEO that half their marketing budget might be going down the drain?

This collective blindness is exactly why Big Adtech thrives. Like skilled casino operators, Google and Meta have made advertising on their platforms irresistibly easy and addictively compelling. The instant gratification of clicks and conversions creates a dopamine hit that keeps marketers coming back for more, regardless of the true cost. Modern marketers have become like gambling addicts at a slot machine, mindlessly feeding coins into the Big Adtech beast while muttering “Just one more campaign.” They’ve morphed from brand builders into collection agents for Big Adtech’s empires, willingly transferring billions in corporate profits to the tech giants’ coffers.

Think of AdWaste as financial diabetes – a chronic condition that slowly but surely destroys business health from within. Just as a sugar addiction leads to devastating health consequences, the addiction to endless acquisition quietly erodes profitability. Like a homeowner repeatedly paying to fix the same leak instead of addressing the root cause, brands keep paying to reacquire the same customers instead of building lasting relationships.

This wilful ignorance doesn’t change reality. AdWaste is as real as gravity – whether one measures it or not, its effects are undeniable. It acts like a hidden tax on growth, silently draining resources that could be invested in product innovation, customer experience, or pure profit. The good news is that unlike many business challenges, AdWaste is both measurable and manageable. We finally have a cure for this corporate affliction.

Measuring the Waste

The start, as with any recovery, is measurement. One cannot manage what you do not measure. As outlined earlier, marketers are paying for four distinct audience segments, but only one represents genuine new customer acquisition. The other three segments – especially known non-buyers and existing customers – represent pure AdWaste.

Here are the critical questions every marketer needs to answer to understand their AdWaste:

  1. Reacquisition AdWaste
  • What percentage of “new” users acquired through paid channels already exist in the CRM database?
  • How many are known non-buyers and existing customers? (These should never have been reacquired; they should have been retained.)
  • What is the total spend on reaching these already-known users? This is Reacquisition AdWaste.
  1. New Acquisition AdWaste
  • Of your genuine first-time users, what percentage provided a digital handle (email/mobile)?
  • The rest are essentially “ghost users” you can never reach again.
  • What is the cost of these unidentifiable acquisitions? This is New Acquisition AdWaste.
  1. True CAC Calculation
  • What is the actual customer acquisition cost (CAC) when measured against truly new identifiable customers?
  • True CAC = Total digital ad spend ÷ New email IDs or mobile numbers added
  • This reveals the true cost of acquiring actionable customer relationships

To summarise: Total AdWaste = Reacquisition AdWaste + New Acquisition AdWaste, where

  • Reacquisition AdWaste = Cost of reaching known users
  • New Acquisition AdWaste = Cost of unidentifiable acquisitions

When calculated this way, the numbers are staggering, revealing a sobering truth about digital marketing efficiency. They shock marketers into realising that their supposedly sophisticated digital campaigns are largely reaching people they already know – at premium prices via competitive auctions. This awakening usually leads to two realisations:

  1. Traditional ROAS optimisation is just rearranging deck chairs on the Titanic
  2. The Only Once theory, with its focus on retention re-engineering, offers a path to sustainable profitability

The measurement of AdWaste isn’t just an academic exercise – it’s the first step toward marketing transformation. Once marketers, CEOs, and CFOs understand the scale of waste in their current approach, the imperative to change becomes clear. The question then becomes not whether to adopt Only Once, but how quickly they can implement NeoMails, SmartBlocks, and AI Twins to make the transition.

3

NeoMails: Brand Hotlines

NeoMails represent a new class of emails with a singular focus: building sustainable hotlines between brands and their customers through daily value exchange and trust building. Unlike traditional promotional or transactional messages that dominate today’s inboxes, NeoMails create daily touchpoints that transform casual customers into engaged brand advocates.

Design Philosophy

At their core, NeoMails are micro-learning vehicles, designed for the attention-scarce digital age. Each mail requires just 15 seconds to consume yet delivers meaningful value through a carefully crafted combination of education and entertainment. Instead of pushing products, NeoMails take recipients on enriching journeys that create daily “Aha” moments – bringing smiles, insights, and a sense of accomplishment. This consistent “user enrichment” value delivery helps imprint the brand in customers’ subconscious minds and creates a “trust relationship”, making traditional marketing messages more effective when they do arrive.

Simply put: User enrichment à Trust relationship à Marketing effectiveness

Anatomy of a NeoMail

Every NeoMail combines five elements:

  1. Smart Subject Lines
  • Embedded Atomic Rewards (Mu) to incentivise opens
  • Streak mechanics to build daily engagement habits and reward loyal attention
  • Clear value proposition for the recipient
  1. Enriching Body Content
  • Non-promotional microcontent focused on user enrichment and knowledge building
  • Personalised learning journeys powered by AI
  • Industry-specific insights and education
  1. Interactive SmartBlocks
  • DataBlocks for zero-party data collection
  • ContentBlocks for attention and engagement
  • Brand-agnostic quizzes, games, and interactive elements
  1. Precision ActionAds
  • Contextually relevant advertising
  • In-email conversion capabilities
  • Targeted based on user preferences and behaviour
  1. Mu Management
  • Real-time rewards balance
  • Redemption opportunities
  • Engagement tracking

Thus, NeoMails systematically build trust through:

  • Consistent delivery of enriching content
  • Transparent value exchange (Mu rewards)
  • Progressive relationship deepening
  • Respectful data collection
  • Relevant, non-intrusive monetisation

Industry Applications

I wrote in NeoMail: Solving the Trifecta of Marketing:

Think of [NeoMails] as digital billboards delivered straight to the inbox – consistent reminders of the brand’s value that, through daily delivery, become part of the customer’s routine. The content strategy for these “microns” (micro newsletters) varies by industry but always focuses on delivering genuine value:

  • A securities firm might share daily investment wisdom from legendary investors
  • A beauty brand could offer seasonal skincare tips and expert advice
  • A travel company could provide destination micro-stories and travel hacks
  • A health insurer might share daily wellness tips and preventive care insights
  • An e-commerce platform could deliver trend updates and style guides, and a streaming service might offer brief entertainment news and viewing recommendations

… This innovation in email design – focusing on in-email actions rather than clickthroughs – allows for multiple engagement points within a single message.

Zero-Effort Implementation

NeoMails operate on a ZeroCPM model, requiring minimal brand involvement:

  • Sent and managed by NeoESPs
  • No upfront costs for brands
  • Revenue sharing from ActionAds
  • Data sharing for improved N=1 personalisation

**

This innovative approach transforms traditional email marketing in several ways:

  1. Shifts focus from clickthroughs to in-email actions
  2. Creates multiple engagement points within each message
  3. Builds habitual engagement through daily value delivery
  4. Generates new revenue streams through ActionAds
  5. Enables progressive profiling through SmartBlocks

By combining these elements, NeoMails solve the fundamental “No Hotline” problem by creating trusted pathways for ongoing dialogue and value exchange. The genius lies in their simplicity: 15 seconds of daily user enrichment builds engagement habits, enables zero-party data collection, and opens new monetisation opportunities. For brands committed to Only Once, NeoMails transform traditional email into an interactive brand relationship platform that makes costly reacquisition unnecessary. Through the ZeroCPM model, this transformation is immediately accessible to brands, requiring no upfront investment. The future of customer engagement isn’t in promotional blasts—it’s in these trusted, enriching daily touchpoints that make every interaction count.

4

SmartBlocks: Unlocking Zero-Party Data

With hotlines established through NeoMails, the next phase of Only Once focuses on intelligent zero-party data collection. SmartBlocks enable progressive profiling for true N=1 Personalisation of marketing messages, microcontent, and ads. This isn’t just another data collection tool—it’s a fundamental reimagining of how brands build customer understanding.

Traditional marketing relies heavily on first-party data—the digital breadcrumbs of user behaviour like searches, page views, and clicks. While valuable, this approach offers only inferred understanding, often missing the “why” behind customer actions. SmartBlocks transform this paradigm by creating direct dialogues with customers. Instead of guessing what customers want based on behaviour, why not build trust and simply ask them?

The Zero-Party Data Advantage

Zero-party data—information customers willingly share about their preferences, interests, and intentions—represents the holy grail of personalisation. Unlike inferred data that tells you what customers did, zero-party data tells you what they want to do. This distinction is crucial: it’s the difference between knowing someone visited a travel site (behaviour) and knowing they’re planning a family beach vacation next summer (intention).

SmartBlocks make this data collection natural and rewarding through:

  • DataBlocks: Embedded modules that collect zero-party data through subtle interactions, such as preference polls or mini-quizzes. These build customer profiles without disrupting the flow of the email content.
  • ContentBlocks: Interactive elements that boost attention and engagement by offering fun, informative activities that align with the user’s interests.
  • Generative Journeys: These adaptive journeys evolve based on user interactions with SmartBlocks, feeding into AI Twins to create increasingly personalised experiences. [see Generative Journeys: Digital Marketing’s New Core.]

The Architecture of Trust

SmartBlocks succeed by creating a transparent value exchange:

  1. For Customers
    • Personalised recommendations
    • Tailored content experiences
    • Loyalty rewards and recognition
    • Control over their data sharing
  2. For Brands
    • Progressive customer profiling
    • Deeper preference insights
    • Enhanced personalisation capabilities
    • Stronger customer relationships

Industry Applications

SmartBlocks adapt across sectors while maintaining their core value proposition:

Retail

  • Style preference selectors
  • Upcoming purchase intentions
  • Category interest indicators
  • Size and fit preferences

Travel

  • Destination type preferences
  • Travel planning timelines
  • Activity and experience interests
  • Accommodation preferences

Finance

  • Financial goal mapping
  • Investment risk tolerance
  • Life stage indicators
  • Product interest signals

Healthcare

  • Wellness goal tracking
  • Lifestyle preference capture
  • Prevention priority indicators
  • Care scheduling preferences

While applications vary by industry, the foundation of successful SmartBlocks implementation remains consistent: building and maintaining customer trust.

Building Engagement Through Trust

Success with SmartBlocks requires a strategic approach to trust-building:

  1. Transparency
    • Clear data usage policies
    • Visible value proposition
    • Control over sharing
  2. Incentivisation
    • Atomic Rewards (Mu) integration for immediate engagement recognition
    • Immediate value delivery
    • Progressive benefits
  3. Consistency
    • Regular interaction opportunities
    • Ongoing value exchange
    • Continuous profile enrichment

**

SmartBlocks represent more than just a data collection tool—they’re a strategic bridge between customer understanding and personalisation at scale. By enabling progressive profiling through natural, rewarding interactions, they solve the fundamental “Not for Me” problem that plagues traditional marketing by enabling true customer understanding rather than mere inference. When combined with NeoMails and AI Twins, SmartBlocks complete the virtuous cycle of the Only Once approach: better data leads to better personalisation, which drives deeper engagement and encourages more data sharing. This creates a self-reinforcing ecosystem where every interaction makes future ones more valuable, ultimately eliminating the need for costly reacquisition.

5

AI Twins: Powering N=1 Personalisation – 1

Every marketer dreams of the perfect customer interaction: delivering exactly the right message, at precisely the right moment, through the ideal channel, to each individual customer—creating a virtuous cycle of repeat purchases and maximised lifetime value. Whether called relevance or recommendations, the goal is to eliminate friction and waste in every customer relationship. Yet today’s marketing reality is built on “brand spam” that abuses the trust and permission customers grant. The inevitable result? Customers withdraw their attention and sever connections, forcing brands into a costly cycle of reacquisition through adtech platforms. The ultimate irony unfolds as brands repeatedly bid against each other at auctions to reach their own lost customers. This wasteful spiral of acquisition, reacquisition, and re-reacquisition can only be broken through true N=1 Personalisation—treating each customer as genuinely unique by building on the foundation of NeoMail hotlines and SmartBlock zero-party data collection.

Personalisation as an idea has been around forever, and yet it is easier said than done. As Mark Abraham and David Edelman write in their book “Personalized: Customer Strategy in the Age of AI”: “Personalization is a do-or-die aspect of corporate strategy. The nature of competition has changed. Data is now plentiful. The shift to digital channels took a quantum leap forward in the early 2020s, spawning millions of new direct customer relationships and a wealth of data about behavior and preferences. With the advent of generative AI, customers can increasingly call on virtual assistants for most of their needs, raising the bar for the level of personalization other channels must offer to stay relevant. Technology is more accessible; many more off-the-shelf tools are available for companies—even ones with more limited budgets—to use, if they can smartly integrate them.”

The authors add: “Personalization [is] what happens when you’re searching on a website and are shown items based not just on what others who searched for that term would like but on what you would like, based on your context, search history, and any items you recently bought. It’s when you phone a call center and the agent has all the information about you and your history with the company at their fingertips (based on the number you are calling from) and can get right to solving your problem. It’s when your favorite sales associate in a store greets you by name, remembers your last purchases and tastes, and creates a tailored shopping experience to delight you. And more to the point, it’s when every sales associate in that store does that with every one of their regular customers. Personalization is creating experiences at scale that get fine-tuned with each successive interaction, empowering customers to get what they want—better, faster, cheaper, or more easily. The difference lies in the way personalized communications build on everything the company learns about the customer over time.”

This table from the book discusses how to assess personalisation:

These ideas are echoed by Joe Park, chief digital and technology officer at Yum: “In the pilot, we delivered emails that were customized at an individual level. When we look at factors like the time of day, the day of the week, the subject line in the email, the content, and more, you can optimize that for marketing use cases like upselling, retention, referrals and even win-back strategies. For example, we know our Pizza Hut consumers have different frequency and timing of orders. Some consumers will order every few weeks and love exploring our menus, while others wait for special occasions like the Super Bowl and typically have large orders. With AI-driven marketing, instead of sending the same offer to everyone as a one-size-fits-all, we can engage each of them with the relevant offers at the right time. Our goal is to give our consumers better-timed offers, personalized content and tailored interactions so they feel understood and don’t receive generic clutter.”

To achieve this level of hyper-personalisation at scale—turning the dream of true N=1 marketing into reality—brands need a breakthrough technology: AI Twins.

6

AI Twins: Powering N=1 Personalisation – 2

I wrote in Digital Twins in Marketing: Magical Minions: “Customer Twins take two primary forms: Segment Twins and Singular Twins. Segment Twins represent groups of customers with similar behaviours, preferences, and characteristics. These virtual cohorts allow marketers to simulate responses to campaigns across different customer segments, test messaging and offers on virtual groups before real-world deployment, and identify trends and patterns within specific customer cohorts. On the other hand, Singular Twins are AI-powered replicas of individual customers, enabling hyper-personalised marketing at scale – true N=1 marketing. These individual twins facilitate predictive modelling of customer journeys and real-time optimisation of interactions across all touchpoints, ensuring each customer receives a tailored experience.”

In AI Twins: The Future of Marketing Intelligence, I wrote: “AI Twins serve as a bridge between the vast sea of data available to marketers and the actionable insights needed to drive effective strategies. They augment human creativity and intuition with data-driven precision, enabling marketing teams to craft more targeted, personalised, and effective campaigns.”

In AI Twins: Digital Customer Representations That Will Transform Marketing, I wrote:

What makes AI twins so powerful is their potential to synthesize data from various sources—customer data platforms, marketing automation tools, social media and ad platforms, and more. In essence, they can marry martech data with adtech insights, providing a deeper view of customer segments that we’ve never had before.

But the real game-changer is when you can directly interact with an AI through a conversational interface. As marketers, we can ask questions, test hypotheses and gain deeper insights into customer mindsets. Want to understand why a segment of customers churned? Have a conversation with an AI twin representing that group. Curious about what your most loyal customers value most about your brand? Ask their segment twin. Wondering what color the call-to-action buttons should be for Gen Z customers? Ask their twin.

This level of interaction allows for a degree of customization and personalization that was previously unattainable. By understanding the distinct preferences and pain points of each segment through these AI-powered conversations, we can develop highly targeted content and strategies.

Gautam Mehra wrote: “Unlike traditional AI tools, AI twins are built from deterministic, observable data—demographics, psychographics, search behaviors and social media interactions. These digital counterparts are more than just replicas of customer segments; they embody how your customers think, feel and react…Now, imagine being able to interact with an AI twin before launching a marketing campaign or product. You could gauge emotional responses, fine-tune your approach based on real-time feedback and ensure you’re heading in the right direction—all before investing time and resources. It’s like having an always-on focus group that gives you real, actionable insights whenever you need them… AI twins…are designed to replicate human thoughts and emotions. They provide insights that go far beyond surface-level data, allowing businesses to anticipate how real customers might react to decisions. By simulating your customers’ mindset, AI twins can help bridge the gap between quantitative data and true customer empathy.”

7

AI Twins: Powering N=1 Personalisation – 3

AI Twins are the third breakthrough that makes Only Once possible, completing the trinity of innovations alongside NeoMails and SmartBlocks. They transform the dream of true N=1 personalisation into reality through a systematic evolution of customer understanding:

The Journey to N=1

  1. Adtech (Starter) Twins: Understanding the Public Self
  • Built from publicly available search and social data
  • Reveal customer attitudes, interests, and conversations
  • Create initial understanding from third-party signals
  • Enable rapid insights without requiring customer data
  1. Madtech (Segment) Twins: Merging Signals
  • Combine first-party behavioural data with public insights
  • Reflect specific customer segment mindsets and patterns
  • Enable more precise targeting and messaging
  • Create foundation for personalised experiences
  • Bridge the gap between martech and adtech data
  1. N=1 (Singular) Twins: The Ultimate Prize
  • Individual digital replicas powered by zero-party data
  • Embody and anticipate each customer’s unique needs
  • Enable true hyper-personalisation at scale
  • Predict future behaviors and preferences
  • Transform marketing from reactive to predictive

The progression from Starter to Segment to Singular Twins mirrors the journey from broad targeting to true N=1 personalisation. Each stage builds upon the previous one, creating increasingly accurate and actionable customer understanding:

  • Adtech Twins provide the baseline understanding
  • Madtech Twins add behavioural context
  • Singular Twins incorporate stated preferences and intentions

This evolution finally makes the marketer’s dream possible: delivering the right message through the right channel at the right time to the right customer. But unlike previous attempts at personalisation, AI Twins don’t just react to past behaviour—they anticipate future needs and preferences. This predictive capability is crucial for the Only Once approach, ensuring that each customer interaction builds upon previous ones while setting the stage for future engagement.

The power of Singular Twins grows with every interaction:

  • NeoMails provide daily engagement opportunities
  • SmartBlocks gather crucial zero-party data
  • AI Twins process this information to create increasingly accurate predictions
  • Each customer interaction makes future ones more relevant

Industry Applications

AI Twins adapt across sectors while enabling true N=1 personalisation for each:

Retail/eCommerce

  • Signal Twins: Track trending preferences and category interests
  • Segment Twins: Model cohort buying patterns and price sensitivity
  • Singular Twins: Predict next purchase timing and product recommendations

Banking/Finance

  • Signal Twins: Monitor financial goals and investment sentiments
  • Segment Twins: Understand life-stage needs and risk appetites
  • Singular Twins: Anticipate product needs and spending patterns

Travel/Hospitality

  • Signal Twins: Track destination trends and travel intent
  • Segment Twins: Model vacation preferences and booking windows
  • Singular Twins: Predict next trip timing and personalised itineraries

Healthcare/Insurance

  • Signal Twins: Monitor wellness trends and health interests
  • Segment Twins: Understand condition-specific needs and behaviors
  • Singular Twins: Anticipate care requirements and prevention opportunities

Media/Entertainment

  • Signal Twins: Track content consumption trends and preferences
  • Segment Twins: Model viewing patterns and genre affinities
  • Singular Twins: Predict content interests and optimal viewing times

Each industry leverages the progressive evolution from Signal to Segment to Singular Twins, building increasingly accurate understanding:

  • Signal Twins provide market context
  • Segment Twins add behavioural insights
  • Singular Twins enable true N=1 experiences

**

AI Twins complete the Only Once trinity, transforming marketing from mass messaging to true N=1 personalisation at scale. The evolution from Signal to Segment to Singular Twins mirrors marketing’s journey from public understanding to individual customer intimacy, converting every interaction into a building block for deeper personalisation. While Signal and Segment Twins provide valuable insights, Singular Twins unlock the ultimate prize: predicting and serving individual customer needs before they’re expressed. Powered by NeoMail hotlines and SmartBlock data collection, these AI Twins ensure each communication is not only relevant but deeply resonant, turning every customer relationship into a continuously evolving journey. For brands committed to Only Once, AI Twins become the key to transforming random transactions into predictable recurring revenue, maximising LTV through superior customer understanding. This creates the foundation for lasting competitive advantages, solidifying the path from costly reacquisition to enduring, profitable engagement and creating a Profipoly.

8

Customer Life Cycle

The Only Once theory comes to life through a systematic approach to customer relationships, eliminating the need for costly reacquisition by building lasting connections from day one. The process follows a natural progression that mirrors human relationships. Here’s how brands can implement the three core innovations—NeoMails, SmartBlocks, and AI Twins—across each phase of the customer journey:

Phase 1: Romance (First 72 Hours)

First Connect

  • Capture email ID/mobile number as the foundation for future engagement
  • Use inputs from Signal Twins to understand broad customer preferences
  • Initialise NeoMail welcome sequence

The First Hour

  • Send personalised welcome NeoMail with embedded SmartBlocks
  • Begin zero-party data collection through engaging interactions
  • Establish user enrichment value proposition and set daily engagement expectations

The Next Two Days

  • Launch daily NeoMails with micro-content and Atomic Rewards
  • Use SmartBlocks to progress profile building
  • Begin building engagement habits through consistent value delivery
  • Deploy Segment Twins for initial personalisation

Phase 2: Marriage (Building Commitment)

First Purchase

  • Send the Marketing mails with product recommendations and offers
  • Deploy SmartBlocks for purchase-related preferences
  • Begin building Singular Twin profile
  • Use NeoMails to maintain daily engagement

Second Purchase

  • Leverage AI Twin insights for personalised recommendations
  • Continue zero-party data collection through SmartBlocks
  • Strengthen daily engagement through personalised NeoMails

Multi-Monetisation

  • Introduce relevant ActionAds from complementary brands
  • Use AI Twins to identify additional revenue opportunities
  • Deploy SmartBlocks to gauge interest in new offerings

Phase 3: Family (Deepening Value)

Give

  • Create exclusive content to deliver through NeoMails
  • Use SmartBlocks to understand evolving preferences
  • Personalise Atomic Rewards based on AI Twin insights

Take

  • Leverage deep customer understanding for referral requests
  • Deploy sophisticated ActionAds based on rich customer profiles

Phase 4: Apart? (Retention and Reactivation)

  • Monitor engagement through AI Twins to predict potential churn
  • In case of dormancy/churn, deploy Reactivation NeoMails sequence based on
  • Deploy Reactivation NeoMails sequence through owned channels, avoiding costly adtech reacquisition. Use history to create targeted value propositions.
  • Use ActionAds to target on other brand emails

The power of this approach lies in its integration. Each innovation plays a specific role:

  • NeoMails create consistent engagement hotlines
  • SmartBlocks enable progressive understanding
  • AI Twins power increasing personalisation

Together, they transform random customer transactions into predictable recurring revenue, eliminating AdWaste while building lasting competitive advantages through superior customer understanding and creating the foundation for Profipoly status.

**

The Only Once strategy represents a paradigm shift in marketing—from the relentless pursuit of acquisition to building enduring customer relationships that maximise engagement and value. By integrating NeoMails, SmartBlocks, and AI Twins across the customer lifecycle, brands can create a cohesive, data-driven approach that transforms customer interactions into a cycle of sustained loyalty and growth. This comprehensive system not only minimises Customer Acquisition Costs (CAC) but also builds a foundation for unparalleled Customer Lifetime Value (LTV). The path forward is clear: marketing’s future lies in replacing reactive strategies with proactive, predictive, and personalised experiences that foster deeper connections and drive lasting profitability.

9

Summary

I asked Claude and ChatGPT to summarise the key ideas of Only Once.

Claude

  1. Core Theory: Only Once
  • Acquire customers exactly once, then focus entirely on retention
  • Inspired by “Let Them Theory” – simple yet profound concept
  • Like E=mc², captures complex idea in simple form
  • Direct challenge to current acquisition-obsessed marketing
  1. The Problem: AdWaste
  • Scale: $350-560B annual waste (50-80% of digital ad spend)
  • Two Types:
    • Reacquisition AdWaste: Paying to reach known customers
    • New Acquisition AdWaste: Acquiring unidentifiable “ghost users”
  • Impact on Brands:
    • 6-10% of total revenues wasted
    • $6-10M annually for $100M business
    • $60-100M for billion-dollar companies
  1. Four Audience Segments in Current Marketing
  1. True New Users (Only valid acquisition target)
  2. Anonymous Returners (Partial failure)
  3. Known Non-Buyers (Pure waste)
  4. Existing Customers (Most wasteful)
  5. Three Core Elements for Implementation
  6. Hotline
    • Continuous connection
    • Two-way engagement
    • Direct access without adtech
  7. Zero-Party Data
    • Voluntarily shared information
    • Trust-based collection
    • Progressive profiling
  8. N=1 Personalization
    • Individual-level customization
    • Predictive capabilities
    • True relevance at scale
  1. Three Key Innovations
  1. NeoMails
    • 15-second daily engagement
    • AMP/CSS technology
    • Five Elements:
      • Smart Subject Lines (with Mu)
      • Enriching Body Content
      • Interactive SmartBlocks
      • Precision ActionAds
      • Mu Management
  1. SmartBlocks
    • DataBlocks for zero-party data
    • ContentBlocks for engagement
    • Progressive profiling
    • Natural, rewarding interactions
  2. AI Twins
    • Three Types:
      • Signal (Adtech) Twins: Public data
      • Segment (Madtech) Twins: Behavioral insights
      • Singular (N=1) Twins: Individual replicas
    • Predictive capabilities
    • True personalization at scale
  1. Customer Life Cycle Integration
  • Phase 1: Romance (First 72 Hours)
  • Phase 2: Marriage (Building Commitment)
  • Phase 3: Family (Deepening Value)
  • Phase 4: Apart? (Retention/Reactivation)
  1. Benefits and Outcomes
  • Elimination of AdWaste
  • Reduced Customer Acquisition Costs
  • Increased Customer Lifetime Value
  • Predictable recurring revenue
  • Path to “Profipoly” status
  1. Implementation Challenges
  • Entrenched interests of Big Adtech
  • Organizational resistance
  • Technical complexity
  • Metric changes needed
  • Cultural transformation required

ChatGPT

1.      Introduction to the Only Once Theory

  • Core Concept: Brands should pay for customer acquisition only once and then pivot entirely to retention.
  • Problem: The current marketing landscape is dominated by the cycle of reacquisition, leading to significant AdWaste, estimated to consume 50-80% of digital advertising budgets.
  • Vision: Shift focus from reacquisition to building long-term, profitable customer relationships.

2. The Problem of AdWaste

  • Definition: Repeatedly targeting the wrong segments (e.g., existing customers, known non-buyers).
  • Statistics: Estimated $350-560 billion wasted annually in digital advertising due to inefficient reacquisition practices.
  • Impact: Brands waste 6-10% of their total revenue on redundant spending, undermining profitability.

3. Why the Problem Exists

  • Dependence on Big Adtech: Companies prioritise easy-to-use ad platforms like Google and Meta for reacquisition.
  • Technological Limitations: Traditional channels lack interactive features for continuous engagement.
  • Data Challenges: Heavy reliance on first-party and third-party data with limited personalisation.
  • Organisational Silos: Separation between acquisition and retention teams leads to disjointed strategies.

4. The Only Once Solution: Three Core Innovations

  • Hotline (NeoMails):
    • Purpose: Daily interactive emails that create consistent brand engagement.
    • Features: AMP/CSS technologies for in-email actions, Atomic Rewards (Mu) for incentivisation, and ActionAds for monetisation.
    • Outcome: Transforms emails into engagement hotlines that reduce the need for reacquisition.
  • Zero-Party Data Collection (SmartBlocks):
    • Purpose: Collect data customers willingly share through interactive, engaging email elements.
    • Mechanism: DataBlocks and ContentBlocks seamlessly embedded in NeoMails for progressive profiling.
    • Benefits: Deepens understanding of customer preferences, enabling personalised experiences.
  • N=1 Personalisation (AI Twins):
    • Definition: True one-to-one personalisation using AI-driven digital replicas of individual customers.
    • Types of AI Twins:
      • Starter Twins (Adtech): Built from publicly available data.
      • Segment Twins (Madtech): Combine first-party data for cohort insights.
      • Singular Twins: Fully personalised twins powered by zero-party data.
    • Function: Predict future behaviour, tailor content, and optimise customer interactions.

5. The Customer Life Cycle Aligned with Only Once

  • Phase 1: Romance (First 72 Hours)
    • Capture contact details, launch personalised NeoMails, and start zero-party data collection.
  • Phase 2: Marriage (Building Commitment)
    • Personalised product recommendations, continued data collection, and engagement maintenance.
  • Phase 3: Family (Deepening Value)
    • Deliver exclusive content and leverage AI for rewards and referrals.
  • Phase 4: Apart? (Retention and Reactivation)
    • Monitor engagement, predict churn with AI, and deploy reactivation strategies through owned channels.

6. The Benefits of Only Once

  • Reduced CAC: Minimises the need for costly reacquisition efforts.
  • Increased LTV: Maximises customer value through sustained engagement and personalisation.
  • Elimination of AdWaste: Redirects resources towards retention and customer experience.

7. Strategic Importance

  • Competitive Advantage: Creates lasting relationships that set brands apart.
  • Profitability Shift: Moves from growth-at-all-costs to sustainable, profitable growth.
  • Future Outlook: Brands that implement Only Once are positioned to become market leaders or “Profipolies.”

8. Conclusion

  • Essence: The Only Once strategy reshapes marketing by focusing on acquiring customers once and investing in building enduring connections.
  • Call to Action: Emphasises the urgency for brands to adopt these innovations to stay competitive.

Thinks 1462

WSJ: “[The US] Government is a giant Industrial Age machine with new legal extruders added every year—at this point, made up of about 150 million words of binding law and regulations. Recent reforms limiting the time and length of environmental-review processes don’t work because they conflict with countless mandates—say to minimize harm to threatened species and historic buildings, to consult with Native Americans thousands of miles away, to give preferences to minority-owned businesses, and more. This machine produces the poor decisions that it was ostensibly designed to avoid. The solution isn’t to oil the machine or rearrange its parts, but to replace it with a 21st-century framework that allows flexible and transparent decisions. Red tape should give way to human responsibility. Simpler frameworks would allow officials to make unavoidable trade-offs needed to achieve public goals. This isn’t radical—it’s how any successful human enterprise works. Nor does it require trusting officials to do the right thing. The only necessary condition is that everyone in the hierarchy must be accountable.”

Mrudul Nile: “If [Indian] politicians are unable to generate employment, it is essentially a matter of economic policy. Bank transfers before elections are nothing but the distribution of cash for votes. This is not a policy solution to the problems of unemployment and poverty, nor is it compensation. Traditionally, politicians would build a constituency by continuously engaging with the people. Most issues, including civic issues, were resolved with the intervention of people’s representatives. This is no longer the case. Today, larger constituencies are built using social media and post-truth narratives. In modern-day politics, a constituency is not confined to a geographical space with a proportional population; it is a demographic imagination of an entire subset of a population, such as the youth or women. While earlier studies show that women were the last to be considered as independent potential voters and it was assumed that most women would vote according to the choice of the family (essentially the male head of the family), this is not the case any longer. Women are important voters and are being wooed everywhere.”

FT: “Bitcoin’s grand vision of a trustless financial system has been reduced to just another entry in the ledgers of the Depository Trust & Clearing Corporation — the massive clearing house that processes nearly all stock trades in the US. In other words, the revolutionary technology meant to bypass the establishment has become another product it controls. The implications for pension funds and their beneficiaries — ie those of us hoping to retire one day — are worrying. While crypto allocations remain relatively small, a precedent is being set. Fiduciaries are increasingly pressured to consider crypto exposure part of a “modern” portfolio. This is despite the fact that its fundamental characteristics remain unchanged. It still produces no cash flows, has no intrinsic value and its price movements are overwhelmingly driven by retail sentiment.”

WSJ: “Among the many items stored in my basement was a box that my mother left me shortly before her death. It contained a number of seemingly random items, most of them passed on to her by her own mother…If the items in the box could talk, I wondered what I would learn about this distant figure in my life…I found myself imagining a box of my own, one that could give my children, grandchildren and maybe even great-grandchildren some insights into my life—not necessarily what I accomplished, but who I was as a person. What would I put in this box, and how would I describe the significance of each item?”

William Shughart II: “Opinions expressed in response to political polls and decisions made in the voting booth differ substantially from ordinary market decisions, where costs and benefits are more closely aligned. Because they have skin in the game, people’s market choices do reveal their preferences. Thus, it should be no surprise that pollsters claiming to forecast hypothetical political preferences often get it wrong.” Part 2.

Looking Back, Looking Forward (Part 3)

Next

If 2024 was the Year of Ideas, then 2025 must (finally) become the Year of Execution. It feels like embarking on a startup journey all over again, armed with bold ideas to transform martech and disrupt adtech. The excitement is palpable, and I often find myself reflecting on the early years of IndiaWorld—venturing into uncharted territory with little certainty but immense hope of creating something innovative and impactful.

These ideas have been years in the making, and their emergence has been anything but linear. It feels like climbing a series of peaks, only to discover more “mountains beyond mountains.” With each idea that is thought through, the next horizon comes into view. What began as concepts like Martech 2.0 and Email 2.0 has evolved and expanded into an interconnected vision of NeoAdtech, NeoMartech, NeoESP, NeoMails, and NeoSaaS. (Somewhere along the way, I must admit, I’ve developed a fondness for the word ‘neo’!)

But ideas alone aren’t enough—they need to be brought to life. Converting this vision into products, revenues, and real-world impact is my defining challenge for 2025. It’s a daunting task, but also an exhilarating one. Just as those early days with IndiaWorld required grit, creativity, and perseverance, this next chapter demands the same. And with these ideas as my compass, I’m ready to take on the climb.

As I look ahead, there are a few aspirations, reflections, and challenges I’d like to take on:

  • Building Netcore into an enduring, great company: I believe we’ve made great strides, but we still have a way to go. I keep asking myself, What does AI-first marketing truly look like? What defines an AI-first B2C or D2C business? And, most importantly, Can we enable brands to create profipolies—profitable monopolies built on retention and sustainable growth? These questions will shape much of what I focus on in the coming year.
  • Writing my next book: The idea of starting another book has been on my mind. It feels like the right time to distil and share my thoughts, weaving together the themes of innovation, entrepreneurship, and marketing for a new era.
  • Spending more time with friends: I’ve come to realise that I spend far too much time in my own company. While introspection has its place, I should also focus on nurturing relationships with friends. I often think of the closing line from the poem I love: “I want to know if you can be alone with yourself and if you truly like the company you keep in the empty moments.” While I find solace in my own company, perhaps it’s time to strike a better balance.
  • Learning something unexpected: I’d like to challenge myself to learn something completely outside my comfort zone—something I’d never have imagined pursuing.
  • Travelling more: It’s been too long since Bhavana, Abhishek, and I have taken a real vacation together. I’d like to make time for meaningful travel that allows us to explore, reconnect, and create lasting memories.
  • Exploring high-quality fiction: While I do a lot of reading, it’s largely focused on business and thrillers. I’ve realised I’m missing out on the depth and richness that great fiction can offer. Expanding my reading horizons could bring fresh perspectives.
  • Listening to podcasts—and perhaps starting one: Podcasts are such a powerful medium for storytelling and knowledge-sharing. I’d like to make time to listen to some great ones, and perhaps even consider starting my own.
  • Giving back in a systematic way: I feel it’s time to think about how I can contribute meaningfully to society. Giving back has always been important to me, but I want to make it a more deliberate and structured part of my life.
  • Reviving the ideas of Nayi Disha and Dhan Vapasi: Sometimes, I wonder if I could bring these ideas to life, creating a pathway toward a truly free and prosperous India. They remain close to my heart, and I feel there’s potential for them to inspire real change.

These thoughts form the essence of what I want to explore in the coming year—balancing professional goals with personal growth, relationships, and impact. It’s an exciting, slightly daunting, but ultimately fulfilling set of challenges that I’m eager to take on.

As always, lots to look forward to. Wish you all a Very Happy 2025.

Thinks 1461

Hollis Robbins: “While we worry about AI replacing human judgment, the real story may be how AI is creating a market for that judgment into a luxury good, available only to those who can pay for the “last mile” of human insight. What do I mean by this?…What I’m calling “the last mile” here is the last 5-15% of exactitude or certainty in making a choice from data, for thinking beyond what an algorithm or quantifiable data set indicates, when you need something extra to assurance yourself you are making the right choice. It’s what the numbers don’t tell you. It’s what you hear when you get on the phone to check a reference. There are other terms for this — the human factor or the human element, but these terms don’t get at the element of distance between what metrics give you and what you need to make a decision. Scale leaves us with this last mile of uncertainty. As AI is going to do more and more matching humans with products and services (and other people), the last mile problem is going to be the whole problem. ”

FT: “Formula One is the world’s most technologically advanced sport. For more than a century, it has been an incubator of future technologies for the automotive, oil and tyre industries. So it is hardly surprising that the motorsport is now attracting companies working in artificial intelligence. This rapidly evolving technology is set to reshape the data-intensive field of Grand Prix racing. Some engineers are predicting AI could one day take on the full design of a car, but the technology is not expected to replace the driver — autonomous car racing debuted earlier this year in a separate Abu Dhabi-funded motorsport series. Each F1 car is fitted with 300 sensors, generating 1.1mn data points per second on the track. And the key to improving the performance of car and driver is to process that huge volume of information as quickly as possible — a task that AI makes easier.”

NYTimes: “Its vibrant purple petals notwithstanding, the saffron flower (Crocus sativus) is valued primarily for what hides beneath those petals: tiny, fiery orange stamens that, once plucked and processed, become the world’s most expensive spice, one that adds sweet flavor and deep golden color to dishes as culinarily disparate as Spanish paella, South Asian pulao and Swedish St. Lucia buns. Known botanically as a triploid owing to its three sets of chromosomes, the saffron crocus can neither grow wild nor reproduce without human intervention. Instead the plant must be propagated and harvested manually, a laborious undertaking that Bronze Age Greeks are believed to have pioneered and that helps account for its price. Today saffron is grown in a latitudinal belt that runs from the Mediterranean to South Asia, with Iran currently contributing about 85 percent of the global supply. But saffron from Kashmir is arguably the most highly prized, says Sana Javeri-Kadri.”

AIER on the economic consequences of populism: “The Americans who succumbed to political lures and failed to wind down, halt, block, or attenuate the New Deal were our great grandparents, or at the very least, our forerunners. Americans today owe it to them to forgive that error – and remember it. After all, they and we are one people. It is possible therefore to extend the sage Anderson’s point. The Great Depression did not endure because God struck America. It endured because our leaders played God. And because we let them.”

Looking Back, Looking Forward (Part 2)

Miscellany

Here is a list of some interesting things that happened in 2024 (in random order):

For Netcore, 2024 has been a year of steady progress, laying the groundwork for the future. As we look ahead, I am optimistic about accelerating this momentum in 2025. This year, we welcomed Siddharth Gopalkrishnan as our COO, whose leadership has already started shaping our path forward. We also earned significant recognition in multiple Forrester and Gartner reports, underscoring the value we bring to the martech landscape.

However, the journey is far from over. The martech industry is increasingly crowded with fierce competition—red oceans abound. To truly stand out, we must focus on identifying and creating blue oceans: untapped opportunities where innovation and differentiation can redefine the game. The road ahead is challenging, but with the right strategies, I believe Netcore can lead this transformation and carve a unique space in the industry.

For me, this year has brought two remarkable new companions: Claude and ChatGPT. Both have been invaluable in helping me think more clearly and write more effectively, shaping and refining my ideas in ways I couldn’t have imagined. I often reflect on a line from Ethan Mollick’s book Co-Intelligence: Think of AI not as a machine, but as a person.’ This perspective has been transformative, turning these tools into collaborative partners in my creative and intellectual journey.

The one routine I cherish most—and which has deepened significantly this year—is my weekend writing ritual. Every Saturday and Sunday, starting at 5 am, I dedicate those tranquil early hours to crafting essays for my blog. On average, it takes me about three hours to complete an essay, but during that time, I enter a state of absolute focus and creativity—what many describe as “the zone” or “flow.” I usually begin with a title and a rough outline, which gradually evolves as the ideas take shape. Words flow into sentences, sentences into paragraphs, and paragraphs into cohesive sections until, finally, the essay emerges. By the time I finish, not only do I have a completed piece, but I often uncover a couple more ideas or topics to explore next. This harmonious cycle of creation and discovery has become the rhythm of my weekends, energising my passion for writing and keeping the well of ideas perpetually replenished.

Thinks 1460

Gwern: “Once you can replicate individual models perfectly, the unit of selection can move way up and you can do much larger groups and packages of minds. That would be an obvious place to start. You can train individual minds in a differentiable fashion, but then you can’t really train the interaction between them. You will have groups of models or minds of people who just work together really well in a global sense, even if you can’t attribute it to any particular aspect of their interactions. There are some places you go and people just work well together. There’s nothing specific about it, but for whatever reason they all just click in just the right way. That seems like the most obvious unit of selection. You would have packages—I guess possibly department units—where you have a programmer and a manager type, then you have maybe a secretary type, maybe a financial type, a legal type. This is the default package where you just copy everywhere you need a new unit. At this level, you can start evolving them and making random variations to each and then keep the one that performs best.”

NYTimes: “Practically any creature that has eyeballs produces two sets of tears: basal and reflex. Basal tears keep the eye moist, while reflex tears are meant to protect the eye from irritants like dust. Humans also shed a third type, fittingly called emotional tears, when they are sad, frustrated, overwhelmed, happy or moved…One of the most common reasons for crying is the absence or loss of a loved one, whether we’re homesick as children, heartbroken in adolescence or grieving a death at any age. We cry over the plights of others, too. These empathetic tears may occur because we are imagining ourselves in other people’s shoes, whether they are friends, strangers or even fictional characters. In fact, this is how scientists study crying: They show people a sad clip from a film and see if it turns on the waterworks.”

Wired: “Physical Intelligence has assembled an all-star team and raised $400 million on the promise of a stunning breakthrough in how robots learn…[It] believes it can give robots humanlike understanding of the physical world and dexterity by feeding sensor and motion data from robots performing vast numbers of demonstrations into its master AI model. “This is, for us, what it will take to ‘solve’ physical intelligence,” Hausman says. “To breathe intelligence into a robot just by connecting it to our model.””

WSJ: “One of the most powerful forces in entertainment has become so pervasive in so many ways that you probably don’t even realize the full extent of its reach. This site best known as a place to watch videos is now the biggest platform for podcasts. Yes, podcasts. Not Spotify. Not Apple. YouTube! Because these days, we don’t just listen to podcasts. Now we watch podcasts. It’s a profound shift that suddenly has the world’s audio giants battling for supremacy in the increasingly valuable world of video podcasts. “It’s becoming all about video,” Daniel Ek, Spotify’s chief executive, told my colleague Anne Steele…The most improbable thing about how YouTube made the podcast market all about video is how swiftly it happened.”