ZeroBase: A New Business Model for the Agentic Marketing Era (Part 3)

In Business

I asked Perplexity for an overview of outcome-based pricing in the business world.

Outcome-based pricing is transforming how businesses think about value creation and client relationships. Rather than charging customers based on features, usages, or hours spent, companies employing outcome-based pricing only get paid for the measurable results they deliver. This approach sharply aligns what clients pay with the business value they receive, creating a win-win dynamic that rewards both performance and partnership.

What Is Outcome-Based Pricing?

Outcome-based pricing means the price of a product or service is directly tied to the results achieved for the customer. For example, a marketing agency might be compensated according to leads generated or sales increases, while a SaaS provider could be paid based on cost savings or efficiency gains clients experience from using their solution. In manufacturing, it may be pegged to uptime or energy savings, and in healthcare, it’s often linked to improved patient outcomes or lower readmission rates.

This model contrasts with traditional methods like flat fees, hourly billing, or usage-based pricing. Instead of paying for inputs or activity, the client only pays when agreed-upon outcomes or KPIs are met.

Key Benefits

  • Perfect alignment of incentives: Both the provider and the customer are invested in achieving success. The provider is motivated to deliver results, while the client is assured payment is tied to real, measurable outcomes.
  • Stronger relationships and trust: Because payments reflect real business value, trust deepens, and clients are less likely to churn. The model typically leads to more collaborative, long-term relationships.
  • Shared risk: This model shifts some risk from the client to the provider, making it easier for customers to say yes—and faster to close deals.

Real-World Examples

  • Technology & SaaS: Intercom’s Fin, an AI customer support solution, charges $0.99 per resolved issue—not by seat or message volume. Customers pay only if the bot solves their problem, making the relationship directly value-based.
  • Manufacturing: Hitachi Rail’s trains-as-a-service lets customers pay based on train punctuality and availability, not ownership—again, tying cost directly to the promised service.
  • Professional Services: Consulting firms and marketing agencies commonly use this model, charging for revenue increases, cost reductions, or campaign performance, so that their success mirrors the client’s success.

Challenges

While intuitive, outcome-based pricing is not without complications:

  • Measuring success: Both sides need to clearly define the outcomes and measure them within a specific timeframe. Ambiguity—such as what exactly counts as a resolved ticket—can cause disputes if not clarified upfront.
  • Attribution: Especially for complex services, proving your solution alone drove the outcome can be tricky.
  • Financial planning: Predicting revenue becomes harder for providers as payment fluctuates with performance. Investors may also require education about this model.

When to Use Outcome-Based Pricing

Outcome-based models work best when:

  • The outcome is easy to define and measure (e.g., sales numbers, cost savings, uptime).
  • The provider and client can align on fair metrics and trust the data.
  • The solution clearly drives the business results.

In today’s competitive and results-oriented business world, outcome-based pricing is gaining ground as a partner-focused, innovation-driving model. By connecting revenue directly to value delivered, companies can build deeper connections, earn premium fees when they over-deliver, and secure client loyalty in the long term.

Thinks 1707

ET: “Is the Indian CMO the next CEO-in-waiting?…In the ever-changing business arena, Indian CMOs are stepping up as influential enterprise leaders, artfully combining the precision of data science with the power of storytelling to propel organizational success. They skillfully navigate a challenging landscape, integrating analytics, innovative thinking, and strategic foresight.”

SaaStr: “AI agents are wrong 30-40% of the time on anything complex. API integrations, edge cases, architectural decisions — they’ll give you confident-sounding answers that are just wrong. What to do: Always ask for multiple approaches. “Give me three ways to build this algorithm” became my default. Push back on suggestions that don’t sound right.  And if it said it’s done something — verify that it actually has.  Don’t move on. Stop treating AI suggestions like they are … right. They’re hypotheses to test.”

WSJ: “Be the reader you want to see in the world…The written word is the most basic building block of all great nations.”

David Brooks: “I applaud ambition, but aspiration sounds a lot more important. It takes courage to build the kind of relationships you’ve never experienced before, to cultivate the kind of virtues you’ve never possessed before. The world doesn’t applaud you as much when you devote yourself to the inner sanctification rather than to outer impressiveness.”

NYMag: “Generative-engine optimization — also known as answer-engine optimization, GEO, or, if you’re feeling limber, LLMGEO — is best understood as an aspirational term, a way for marketers to assure their employers and clients that, in a world where people spend their days chatting with fast-developing AI chatbots that have eaten the entire web and can regurgitate it on demand, there are still ways to get an edge for their brands. Chatbots are trained on the web, continuously scrape the web, and often still link to the web; some have search features built in or call on search engines in the course of conversations with users. In other words, the optimizers still have hope, and early folk wisdom is taking shape, making the rounds, and finding its way into practice. “LLMs, like Google AI mode or ChatGPT, will use what is called a fan-out technique with lots of queries covering every angle,” says Solis. “Then they will match these variations not with whole pages but with passages, or chunks,” she said. In response to a question, in other words, a chatbot will tend to summarize and excerpt, with citations rather than prominent links. If you want to get cited, she says, you should publish content with that in mind. A lot of SEO-driven content “was very wordy,” she said, which doesn’t help with being scraped by AI. Now, she said, publishers should “structure the content in an easier way to be grabbed” — in citable chunks, with clear authorship.”

WSJ: “[Managers are more disengaged now because] they already had a lot on their plates, including meeting senior leadership’s expectations, communicating changes, administrative things like timesheets, motivating high performers, developing star performers and keeping people. And now suddenly you’ve got a workplace where there are all these disruptions happening. You’ve got postpandemic job reshuffling, a hiring boom and then bust, restructuring of teams, changing and sometimes shrinking budgets. The advent of AI and digital transformation. Flexible work expectations, which put more burden on managers to keep track of people. So the combination of an already high-demand job combined with these recent changes is a big reason.”

ZeroBase: A New Business Model for the Agentic Marketing Era (Part 2)

Madhavan Ramanujan

In Monetizing Innovation: How Smart Companies Design the Product Around the Price by Madhavan Ramanujam and Eddie Hartman, the authors write:

Price is more than just a dollar figure; it is an indication of what the customer wants—and how much they want it. It is the single most critical factor in determining whether a product makes money, yet it is an afterthought, a last-minute consideration made after a product is developed. It is so much of an afterthought that companies frequently call us and say, “We built a product—oops, now we need your help in pricing it.”

To boil it down, these companies conduct product development this way: They design, then build, then market, then price. What we will teach you in this book is to flip that process on its head: Market and price, then design, then build. In other words, design the product around the price.

…The most successful product innovators we know start by determining what the customer values and what they are willing to pay, and then they design the products around these inputs and have a clear monetization strategy that they follow through with.

Scaling Innovation: How Smart Companies Architect Profitable Growth is a recently published sequel. In a recent podcast with Lenny Rachitsky, Madhavan Ramanujan discusses how to build enduring businesses by dominating both market share and wallet share. A post on X by Lenny summarises the essence of pricing:

From the post:

The bottom left, that is the quadrant where your attribution is low, and your autonomy is low. In that situation, the best pricing archetype is seat-based or a subscription model, because you’re not able to attribute a lot of value to what you bring.

The bottom right quadrant, those are companies that can prove more attribution, and show what they actually bring to the table. But they’re still not in a fully autonomous mode, there are still humans in the loop. A hybrid pricing model is the best option for them.

If you look at the top left quadrant, those are products that are very autonomous, but are not strong on attribution. These tend to be mostly backend or infrastructure kind of products. In that situation, you need to be on a pay-for-what-you-consume model.

The quadrant that you really want to be in is the top right one. That’s the outcome-based pricing model. This is where you have great autonomy and great attribution. You are not only charging for work delivered, but you’re charging for work delivered that was delivered by AI with no humans in the loop.

From the podcast: “[Outcome-based pricing] means you’re not only charging for work delivered but you’re charging for work delivered that was delivered by AI without no humans in the loop… In AI, you can actually charge 25 to 50% of the value you bring to the table… because it is autonomous, you’re doing it with the AI, no humans in the loop… With AI finally founders can solve the attribution problem… you get a lot of pricing power.”

To summarise the key ideas: Outcome-based pricing is the most powerful model for AI products, enabling companies to charge based on measurable, attributable value delivered without human involvement. This approach is ideal when AI systems operate autonomously and impact key business metrics. Currently, only a small percentage of companies use this model, but adoption is expected to grow significantly. Transitioning to outcome-based pricing requires building attribution mechanisms, such as ROI models and value audits. While hybrid pricing models dominate today, founders should aim to evolve toward outcome-based pricing by co-creating business cases with customers and aligning pricing with real-world outcomes rather than inputs or access.

Thinks 1706

WSJ: “Call it an “age of infrastructure,” in which companies spend vast sums on actual stuff. Primarily that’s the gigantic data centers filled with tiny chips, and everything that connects and cools them, but it also includes factories, real estate and energy. It’s reminiscent of the age of business titans and “robber barons” who dominated railroads, steel and other enterprises. And as happened then, today’s massive companies, with their ability to spend (and borrow), are making their moats even deeper and wider. Even formidable competitors, such as OpenAI, are hard-pressed to keep up.”

David Brooks: “The central argument of the 21st century is no longer over the size of government. The central argument of this century is over who can best strengthen the social order.”

Billy Beane: “The data’s out there for everyone, information’s out there for everyone. Really, executing on that data is the most important thing. And some teams do it better than others.”

FT: “The potential benefits of widespread AI use are enormous. By speeding up routine tasks, it can free up leisure time or allow busy people to dedicate time to more involved activities. The technology’s ability to process vast amounts of data also means it can accelerate research and development processes, and expand human knowledge. It has made significant progress in brain mapping and mathematical reasoning. However, the explosion of instant, easy access, AI-driven answers has its potential downsides, too. A particular concern is “cognitive offloading”. This is the idea that frequently outsourcing mental tasks to smart technology can cause our memory and problem-solving skills to atrophy. One example is the “Google effect”: research has already found that individuals can end up depending on search engines as a source of knowledge rather than remembering details for themselves. The risk with powerful AI chatbots, when overused, is that having the bulk of our writing, analysis and creative tasks done for us may mean we engage in less reasoning over time.”

Andrew Batson: ” India has not solved its outstanding problems in agriculture and manufacturing, the fact that the services boom is strong enough to power 7%+ aggregate growth is pretty impressive. It’s hugely important that India now has a self-reinforcing growth cycle in foreign and private-sector investment and exports. It’s almost reminiscent of what happened in China in the 2000s after its WTO entry, even if India’s cycle is mostly in services, which have fewer spillovers to the rest of the economy than manufacturing. If there is an alternative school of thought to the one focused on trying to “be like China” in terms of macro aggregates, it is that India should focus on building on what is already working. That means not just facilitating the tech boom, but also making more sectors outside IT services attractive to investment from both domestic and foreign businesses, and trying to steadily improve state capacity and public services.”

ZeroBase: A New Business Model for the Agentic Marketing Era (Part 1)

Pricing Options

The evolution towards AI agents and agentic marketing is creating fundamental shifts in how businesses operate and monetise. As artificial intelligence becomes increasingly capable of autonomous decision-making and execution, traditional business models are being disrupted across industries. The companies that will thrive in this new landscape are those that can adapt their revenue strategies to harness the unique capabilities of AI agents.

Subscription-Based Agent Services: The New Foundation

Subscription-Based Agent Services are becoming dominant because they align perfectly with the continuous, always-on nature of AI agents. Unlike traditional software that requires human intervention, AI agents work around the clock, constantly learning and optimising their performance. Rather than one-time purchases, businesses are moving toward recurring revenue models where customers pay monthly or annually for agent capabilities that improve over time.

This model creates predictable revenue streams while ensuring customers benefit from continuous improvements. As AI agents learn from more data and interactions, their value proposition strengthens, justifying ongoing subscription fees and reducing churn rates.

Paying for Results, Not Tools

Outcome-Based Pricing is gaining significant traction, fundamentally changing the risk equation in business relationships. Instead of paying for a marketing automation platform with uncertain returns, companies now pay based on qualified leads generated or revenue directly attributed to AI-driven campaigns. This model shifts risk to the service provider but enables them to command premium pricing when they deliver exceptional results.

The beauty of outcome-based pricing lies in its alignment of interests. Service providers are incentivised to maximise client success rather than just feature adoption. This creates stronger, more collaborative partnerships and often leads to higher customer lifetime values despite lower upfront costs.

The Platform Economy Meets AI

Platform Ecosystems are emerging as another powerful model, where companies create marketplaces for specialised AI agents. Major players like Salesforce and HubSpot are evolving beyond traditional CRM providers to become platforms where third-party AI agents can plug in seamlessly. These ecosystems operate on revenue-sharing models between platform owners and agent developers, creating network effects that benefit all participants.

The platform approach allows for rapid innovation without requiring the platform owner to develop every capability in-house. Specialised AI agents can address niche use cases while contributing to the overall ecosystem’s value proposition.

Data as the New Oil

Data Monetisation Models are becoming increasingly sophisticated as companies recognise that high-quality, proprietary data is essential for training and improving AI agents. Organisations sitting on valuable datasets are discovering new revenue streams by licencing this information to AI developers. This is particularly valuable in B2B contexts where industry-specific data provides crucial competitive advantages.

The key is ensuring data quality and relevance while maintaining privacy and compliance standards. Companies that can consistently provide clean, actionable data will find themselves in high demand as AI adoption accelerates.

The Transition Strategy

Hybrid Human-AI Services represent a crucial transitional model for businesses and customers not ready for full automation. These services combine AI efficiency with human oversight and creativity, allowing companies to charge premium prices while building trust during the adoption phase. This approach helps customers gradually become comfortable with AI capabilities while maintaining the human touch they value.

**

The Value Creation Imperative

The most successful companies in this new era will be those that can demonstrate clear ROI through measurable improvements in efficiency, personalisation, or customer outcomes. The key differentiator isn’t just cost reduction—though AI agents excel at that—but value creation. AI agents that can generate entirely new revenue streams or dramatically improve customer experiences will command the highest premiums and create the most sustainable competitive advantages.

The future belongs to businesses that can prove they’re not just cheaper alternatives, but genuinely better solutions that deliver measurable value in ways that weren’t previously possible.

Thinks 1705

Alex Tabarrok: “India’s government job system squanders talent, feeds on obsolete and socially-inefficient prestige hierarchies, and rewards years of sterile preparation with diminishing returns. It’s inefficient, of course, but behind the scenes it’s devastating to the young.”

Mint: “Voice notes are reshaping how we care, connect and communicate. They’re an ideal tool for the lonely, the anxious, and the digitally overwhelmed.”

FT: “Who wins elections, whose ideas spread, whose companies capture attention — the answer is often less rational than we think. The messenger often matters more than the message. Possibly the best rule of politics is that, in a genuine two-horse race, the more charismatic candidate will win. Barack Obama beat John McCain and Mitt Romney. Donald Trump beat Hillary Clinton and Kamala Harris. Joe Biden (2020 vintage) beat Trump. It’s the personality, stupid.”

CollabFund on Ferrari: “By adhering to its founder Enzo Ferrari’s “scarcity dictum” that declares, “Ferrari will always deliver one car fewer than the market demands.” Delivering one fewer than the market demands —How many businesses can say they do that? In my experience, very few. In fact, many do precisely the opposite. Why? Because more is almost always considered better. Size, scale, and growth are seductive. It is what attracts new investors and fresh capital. It is what grabs attention and headlines.

NYTimes: “ChatGPT wasn’t a therapist, although it sometimes was therapeutic. But it wasn’t just a reflection, either. In moments of grief, fatigue or mental noise, the machine offered a kind of structured engagement. Not a crutch, but a cognitive prosthesis — an active extension of my thinking process. ChatGPT may not understand, but it made understanding possible. More than anything, it offered steadiness. And for someone who spent a life helping others hold their thoughts, that steadiness mattered more than I ever expected.”

Marketing’s AI-Native Future: The Rise of Agentic Systems (Part 3)

Endgame

Marketing’s transformation follows a predictable pattern that echoes every major technological revolution: what seems impossible becomes inevitable, then ultimately invincible for those who master it first. We’re witnessing this exact progression as marketing evolves from human-driven campaigns to superintelligent autonomous systems.

Impossible: Breaking Marketing’s Sound Barrier

For decades, marketing faced what seemed like an insurmountable physics problem: the inverse relationship between growth and profitability. Higher growth required higher customer acquisition spending, which reduced margins. Better margins required reducing acquisition spend, which stunted growth. The Rule of 40—where revenue growth plus profit margin exceeds 40%—remained marketing’s mission impossible.

The AdWaste crisis made this worse: 70% of performance marketing budgets were spent reacquiring customers brands already had. Companies saw marketing spend growing faster than revenue, trapping them in an escalating cycle of platform dependency. The mathematics seemed unbreakable: pay 20-30% revenue taxes to Google, Meta, and marketplaces, or accept stagnant growth.

Traditional solutions only shifted the problem. Better creative might improve conversion rates temporarily, but auction dynamics would quickly bid away any gains. Influencer marketing and content marketing provided some relief but couldn’t scale to replace paid media dependency. The fundamental economics remained unchanged: growth required paying the platforms, and paying the platforms limited profitability.

Inevitable: The Convergence of Breakthroughs

Three technological breakthroughs converged to make the impossible inevitable. First, agentic AI systems capable of true autonomous operation—not just automation, but intelligent agents that plan, execute, and optimise without human intervention. Second, authenticated identity targeting that eliminates cookie-based waste while respecting privacy. Third, interactive email technology that transforms static messaging into dynamic, engaging experiences.

The Best-Rest-Test-Next framework provided the strategic foundation, recognising that different customer segments require fundamentally different approaches. The Best 20% customers are 3X more valuable than Rest and 12X more valuable than Test customers. This insight enabled precision resource allocation: AI Agents Collective for hyper-personalising the Best, Progency for systematically converting Rest, and NeoN for slashing acquisition waste.

The economic breakthrough came through parallel optimisation: growing revenue through better retention while reducing marketing spend through AdWaste elimination. The result: profit margin gains that make Rule of 40 performance inevitable rather than impossible. Marketing transforms from cost centre to measurable profit engine.

Invincible: The Compound Advantage

Agentic Marketing creates compound advantages that become invincible over time. As AI agents accumulate more customer data and interaction history, their personalisation capabilities improve exponentially. Each conversation, each transaction, each micro-moment of engagement feeds back into the system, making future interactions more relevant and effective.

The attention moat deepens daily. Brands controlling owned channels through The Brand Daily build stronger customer relationships that can’t be arbitraged away by platform algorithm changes or auction price increases. This owned attention becomes the foundation for everything else: better data collection, more effective personalisation, higher conversion rates, and stronger customer lifetime value.

The network effects are unprecedented. NeoN’s cooperative advertising model becomes more valuable as more brands participate—larger audiences, better targeting precision, more inventory options. The AI Agents Collective becomes more intelligent as it processes more customer interactions across the network. Progency’s outcome-based model means success compounds: better results lead to more clients, more data, and even better AI models.

The strategic superiority is decisive. While competitors remain trapped in Legacy Marketing’s reacquisition hamster wheel, Agentic Marketing practitioners enjoy systematic advantages: lower customer acquisition costs, higher retention rates, better personalisation, and superior profit margins. The gap doesn’t narrow—it widens with each passing quarter.

This is about fundamental business model transformation. Companies implementing Agentic Marketing don’t just outperform competitors; they operate in entirely different economic categories, achieving Profipoly status where sustainable competitive advantages compound through superior customer relationship technology.

The impossible has become inevitable. The inevitable is becoming invincible. Agentic Marketing is coming to transform business.

Thinks 1704

Forbes on Robinhood: “First Vlad Tenev blew up the brokerage industry’s fee model. Now, thanks in part to his full-on crypto embrace, he has increased his fortune sixfold to $6 billion as he embarks on a global financial services takeover with tokenized stocks, AI-powered investing and a bid to own the rails of the looming $124 trillion generational wealth transfer.”

Bloomberg: “Google is playing catch-up and doing rather well at it. It has protected its advertising revenue, which in the last quarter was up 12% to a record-high $54.2 billion compared with the period a year earlier. Its AI and cloud business faces supply constraints, warranting an additional $10 billion in capital expenditure, bringing it to $85 billion for the year. It recently added “AI Mode” to its search engine, which is like AI Overviews on steroids. The company has barely started to integrate AI across its varied products like Gmail and Maps — the Financial Times noted that 15 distinct Google products have more than 500 million users. Executives say they will be able to monetize all of these innovations quickly. The company has less to say about what happens to the businesses that rely on Google traffic to stay alive, in turn providing the content that makes smart AI possible. The shift is profound: Google’s creation democratized the web, making it possible for an ecosystem of new sites and services to be found and supported. Now, the company’s strategy is to make it so users need to visit only Google. “We have to solve the business models for the varying players involved,” Sundar Pichai, Alphabet’s CEO, said in a call with analysts without elaborating.”

Samir Varma: “Love marriages have increased from 5% in the 1960s to about 10-15% in urban areas today — still shockingly low by global standards. Nationally, the figure hovers around 3-8%. But “arranged marriages” now often mean “arranged introductions” followed by months of WhatsApp courtship. The old system where bride and groom met on the wedding day? That’s mostly dead except in the most conservative pockets. Yet the infrastructure of tradition remains ironclad. The median age of marriage for women is still just 22.1 years. Intercaste marriages, despite all of India’s supposed pluralism, hover around 5%. And 95-98% of Indians still marry within their religion.”

FT: “What makes us buy a luxury item? I’ve spent nearly two decades considering the moment that triggers an individual sale. No one needs a luxury: the purchase is instead propelled by aspiration, status, self-affirmation and desire. Then, there’s the slightly transgressive thrill of spending lots of money on something you simply want. The sweaty, credit-card-at-the-ready denial of what others might interpret as a reasonable spend. Some will never feel the impulse. Others know it well. They, like me, are probably familiar with the splurge index of things we are prepared to sacrifice — food, socialising, taxis — in order to justify a spend. For many years there was a consensus among retailers that the gateway price to snagging a new client was around £250. Having made that psychological leap on that first purchase, so went the thinking, the customer would then return.”

Marketing’s AI-Native Future: The Rise of Agentic Systems (Part 2)

The 4 A’s

Agentic Marketing is powered by four breakthrough innovations that work in harmony to create marketing superintelligence. These “4 A’s” represent fundamental advances that evolve from foundational NeoMarketing concepts to sophisticated Agentic systems. The progression shows how Attention (Daily Owned Engagement through The Brand Daily) evolves to My Brand Daily, Autonomous (Automation via AI Agents Collective) becomes Emergent superintelligence, Alpha (Guaranteed Outperformance through Progency) transforms into Self-Optimising Systems, and Ad-Coop (Cooperative Networks via NeoN) advances to Predictive networks powered by Customer Twins.

Attention: The Foundation of Everything

Attention is upstream of transactions, upstream of everything. In the age of attention recession, where 9 out of 10 brand messages are ignored, controlling attention becomes the ultimate competitive moat. The Brand Daily represents this evolution—moving beyond promotional noise to daily value-driven relationship messages that command 15-60 seconds of quality attention daily.

Email emerges as the unexpected champion in this attention war. While WhatsApp costs 50-75X more and push notifications require app installs, email remains universal, cost-effective, and versatile. 13 crore Indians click on at least one email monthly—that’s the entire transacting population. The inbox becomes the new search engine, answer engine, commerce engine, and operating system of engagement.

The Brand Daily transforms emails from static broadcasts into dynamic, interactive experiences using AMP technology. These aren’t newsletters—they’re habit-forming engagement vehicles that build mental salience through consistent value delivery. In the Agentic evolution, this becomes My Brand Daily—a personalised engagement infrastructure that serves as the Kernel for all customer interactions. Logo visibility plus top-of-mind recall plus frictionless engagement equals brand habit, creating owned attention that no platform can switch off.

Autonomous: Emergent Intelligence

The evolution from AI Agents Collective to Emergent superintelligence represents the leap from specialised agents working together to an emergent system that exceeds the sum of parts. The Futuristic Agentic Core combines the Co-Marketer orchestrator, Segment Agents, Content Agents, Insights Agents, and Shopping Agents within a self-organising ecosystem that creates intelligence beyond human capabilities.

The breakthrough lies in creating a “Department of One for Segment of One”—individual customer twins that serve as digital representations capable of predicting next best actions with unprecedented accuracy. Each customer twin operates as a dedicated mini-marketing department, understanding individual preferences, purchase patterns, life events, and micro-moments of intent to deliver perfectly timed, personally relevant experiences.

These customer twins don’t just respond to behaviour—they anticipate it. By continuously learning from every interaction, transaction, engagement signal, and even external inputs, they develop predictive models that identify the optimal product, message, channel, and timing for each individual. The result transforms marketing from reactive campaigns to proactive relationship orchestration at the individual level.

Alpha: Self-Optimising Systems

Progency evolves into Self-Optimising Systems that represent the shift from cost centre marketing to measurable profit engine. Rather than charging for time or media, these systems align incentives through outcome-based execution models that combine AI Agents with human expertise. The economic model becomes revolutionary: instead of paying monthly fees regardless of performance, brands pay based on incremental revenue generated through self-improving algorithms.

The economic model is revolutionary: instead of paying monthly fees regardless of performance, brands pay based on incremental revenue generated. Progency transforms underperforming customers (infrequent buyers, qualified leads gone cold) into growth engines, systematically converting Rest customers to Best through AI-powered personalisation and expert strategy.

The continuous improvement (Kaizen) model ensures performance compounds over time. As the AI agents learn more about customer behaviour and preferences, conversion rates improve, cost-per-acquisition decreases, and lifetime value increases. The result: Alpha returns that beat market benchmarks.

Ad-Coop: Predictive Networks

The evolution from NeoN’s cooperative networks to Predictive systems powered by Customer Twins represents the ultimate advancement in acquisition efficiency. These predictive networks use authenticated identity targeting combined with individual customer digital twins to create anticipatory marketing that responds to customer intent before it’s explicitly expressed.

The cooperative model becomes predictive: beauty brands don’t just reach fashion audiences—they anticipate when fashion customers will need beauty products. ActionAds enable complete transactions directly within partner emails, while Customer Twins enable predictive behaviour modelling that dramatically reduces acquisition costs by 30-50% compared to traditional adtech while enabling anticipatory engagement.

**

These 4 A’s work synergistically: Attention creates the engagement foundation, Autonomous systems optimise at scale, Alpha models align incentives with outcomes, and Ad-Coop networks provide efficient acquisition. Together, they enable systematic, sustainable, profitable growth that was previously impossible.

Thinks 1703

Bloomberg: “India’s total astrology market is estimated to be worth more than $7 billion, and the digital segment alone is expected to grow tenfold by March 2030, according to Bengaluru-based Redseer Strategy Consultants last year.”

FT: “Game development is a tiresome process. Developers make something they think is good, only to discover that playtesters think it is comprehensively bad, difficult to understand or, worst of all, no fun. Then the developers revise. “About 70 per cent of the game is a rewrite. About 50 per cent is a rewrite of a rewrite,” Kurvitz estimated in 2020. The process repeats until the developers run out of money and must publish something, at which point they too may have become convinced of all the ways in which what they have made is very bad.”

Economist: “India’s very own colonial mindset [is] a deeply held belief—among leaders and citizens both—that the state sits at the centre of society and must be in control of it. In Hindi this vision of the state is called the maibaap sarkar, or “mother-father government”. The maibaap sarkar nurtures and protects, but it must also discipline and punish. The colonial mindset is distinct from the colonial hangover. The hangover describes vestiges of the past that have never been eradicated. It can be found everywhere, from the country’s elite gymkhana clubs to its old-fashioned courts. The mindset evolved in part because of deliberate decisions taken during the creation of the new Republic of India. About a third of its constitution borrows directly from the Government of India Act of 1935, which was passed by Britain’s Parliament and laid out the principles by which the colony would be governed. Jawaharlal Nehru, India’s first prime minister, described it as a “charter of slavery”. The constitution is colonial in that it takes heavily from a colonial document, writes Arghya Sengupta, a constitutional scholar, in his book “The Colonial Constitution”. But it is also colonial “in a more conceptual sense: it sets up a government that towers over the citizen much like colonial governments tend to do.””

Ethan Mollick: “One thing you learn studying (or working in) organizations is that they are all actually a bit of a mess. In fact, one classic organizational theory is actually called the Garbage Can Model. This views organizations as chaotic “garbage cans” where problems, solutions, and decision-makers are dumped in together, and decisions often happen when these elements collide randomly, rather than through a fully rational process. Of course, it is easy to take this view too far – organizations do have structures, decision-makers, and processes that actually matter. It is just that these structures often evolved and were negotiated among people, rather than being carefully designed and well-recorded….The Garbage Can represents a world where unwritten rules, bespoke knowledge, and complex and undocumented processes are critical. It is this situation that makes AI adoption in organizations difficult, because even though 43% of American workers have used AI at work, they are mostly doing it in informal ways, solving their own work problems. Scaling AI across the enterprise is hard because traditional automation requires clear rules and defined processes; the very things Garbage Can organizations lack.” [via Arnold Kling]