The Architects of Experience

Building for Consumers In the Age of AI

Gen AI’s meteoric rise is both well underway and very early relative to the potential. As builders and investors race to uncover where the long-term, durable business opportunities lie, we think it’s valuable to take stock in what defined success in the prior consumer platform shifts in order to best anticipate what will win tomorrow. No doubt what comes next will be distinct from the mobile and internet eras, surprising us in ways we can’t yet imagine. But we believe that to see what’s next, you have to understand the present so clearly that the future becomes an inevitability.

The last three decades have brought unprecedented change to consumer tech business models, with the winners of each new wave systematically displacing industry incumbents through greater efficiency, scale, and new experiences. From the internet to the ubiquity of mobile, and now the emergence of generative AI, each wave has not only redefined what consumers expect but also how companies compete.

Today, AI is in a messy, creative period of boundless experimentation and excitement. But the generational companies of tomorrow are getting started today. With the right framework for understanding fundamental platform shifts, we can see the signals of what types of models, behaviors, and founders will ultimately dominate this next era of transformational opportunity.

Let’s get to it:

PART I: The Forces of Reinvention: Internet, Mobile, and AI

PART II: The Evolution of Business Model Advantages: How Each Generation of Disruptors Leveraged Key Advantages to Win

PART III: The Builders, The Designers, and The Architects: Who Wins in Each Era?

PART IV:  User Adoption Challenges and Pathways to Overcoming Them

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PART I: The Forces of Digital Reinvention: Internet, Mobile, and AI

The Internet Boom: Aggregation, Disintermediation, and the Rise of Digital Commerce

The initial wave of internet-driven transformation, starting in the late 1990s through the early 2000s, dismantled traditional business models built around physical infrastructure, localized transactions, and high-friction distribution channels. Companies that thrived in the pre-internet era—like Walmart, Blockbuster, and traditional travel agencies—relied on centralized, asset-heavy models that required significant human intervention.

The internet upended this by introducing disintermediation and aggregation, allowing consumers to bypass middlemen and directly access products, services, and information. Amazon disrupted brick-and-mortar retail with its vast, centralized ecommerce marketplace. eBay and Craigslist made newspaper classifieds obsolete by enabling peer-to-peer selling at scale. Expedia and Priceline challenged the value of travel agents by aggregating flights and hotels into a single user-controlled interface.

These digital-first companies provided better experiences for customers in ways that traditional businesses could not:

• Lower Costs: Digital distribution eliminated overhead costs associated with physical locations and manual processes.
• Greater Convenience: Consumers could shop, book travel, and access content from their homes rather than relying on in-person transactions.
• Increased Access & Choice: Aggregated platforms allowed consumers to compare options in a way that physical marketplaces never could.

But while these companies reshaped industries, they were still primarily desktop-based and required a non–trivial level of invention from consumers. The next major shift—mobile—would take convenience to an entirely new level.

The Mobile Revolution: Instant Access and the Behavioral Dependencies

By the late 2000s and early 2010s, mobile technology reshaped consumer behavior once again, taking business models from online to on-demand and always available, shaping new, sticky behaviors along the way. While the internet had removed barriers of physical location, mobile removed the barrier of time, creating a world in which commerce, entertainment, and communication could happen instantaneously, anywhere.

Retail became mobile-first, with companies like Instagram Shopping and Shopify’s Shop enabling social commerce and impulse-driven transactions. Instacart and Uber redefined utilities by leveraging real-time availability and geo-location. Streaming services like Netflix and Spotify took media consumption from fixed electronics to completely portable, allowing users to access entertainment wherever they were. The thrill of instant gratification quickly turned into expectation, raising the bar for consumer experiences across every category.

What made mobile-driven businesses fundamentally better than their internet-era predecessors?

• Immediate Transactions: One-click purchases, real-time booking, and mobile payments (e.g., Apple Pay, Venmo) eliminated the last pain points of digital transactions.
• Personalization: Apps could now track user behavior, location, and preferences to offer more relevant experiences.
• Push Engagement: Instead of waiting for users to visit websites, mobile companies could proactively engage consumers through notifications and recommendations.

This shift made businesses more integrated into everyday life, creating behavioral dependencies that set the stage for the next major leap: AI-driven business models that anticipate, automate, and enhance human decision-making.

The AI Era: Intelligent, Personalized, and Predictive Experiences

Now, we are entering the next great transformation—AI and automation. Unlike the internet and mobile waves, which focused on connectivity and convenience, AI is about intelligence and execution, removing the need for consumers to make repetitive decisions or even actively search for solutions in the first place.

AI-native companies build on the foundations of digital and mobile commerce, but shift away from involved engagement toward proactive, predictive, and automated interactions.

• Commerce is becoming conversational. AI-powered assistants like Shopify Magic, Perplexity AI, and soon-to-launch Daydream are replacing search-based shopping by suggesting products before consumers even ask.
• Entertainment is evolving beyond curation into creation. AI-driven tools like Runway AI are making it possible for users to generate video and audio content at scale, reducing the need for traditional production pipelines.
• Finance is shifting from manual decision-making to intelligent automation. AI-driven platforms like Origin and Monarch Money are making investing and budgeting more accessible by recommending and executing actions based on individual goals.
• Customer service is moving from reactive to predictive. AI chatbots and virtual assistants anticipate user needs and solve problems before they arise through platforms like Decagon and Sierra.

Why is this model superior to the mobile-driven businesses of the last decade?

• Little to No Friction: Instead of enabling users to seek out information, AI-driven systems preemptively provide insights, options, and actions.
• Mass Personalization at Scale: AI makes it possible to customize experiences for millions of users individually, rather than relying on broad segmentation.
• Automation of Repetitive Tasks: Consumers can focus on higher-value activities while AI handles transactions, bookings, and communications.

What This Means:

If the internet era was about access, and the mobile era was about instantaneity, the AI era is about effortlessness—removing human inefficiency, making transactions invisible, and anticipating needs before they are even expressed.

Each shift in technology has replaced outdated, less efficient legacy players with new entrants that better serve the modern consumer. The companies leading this next transformation are not just improving existing experiences; they are fundamentally reimagining the experience itself. The winners of this new era will be those who eliminate friction, build self-evolving systems that get smarter with every interaction, and create value for consumers before they even realize they need it. 

In this sense, AI is more than just a tool—it is becoming the business model itself.

PART II: The Evolution of Business Model Advantages: How Each Generation of Winners Manipulated Key Advantages to Win

Across each platform shift—the internet era, the mobile revolution, and now AI—the most successful companies have thrived not just because they introduced new technology, but because they mastered and optimized specific business model advantages that reinforced their market dominance.

Each generation’s winners leaned heavily on one or more core advantages of their technology wave—whether it was network effects, data scale, personalization, or automation—to create self-reinforcing, high-retention ecosystems that were designed to structurally improve with time. The companies that succeeded weren’t just tech innovators; they were business model innovators, leveraging new economic advantages that made them very difficult to challenge.

1st Generation: The Internet Era (1995-2010) – Aggregation & Disintermediation

Core Business Model Advantages: Network Effects, Data Scale, and Asset-Light Scalability

How It Worked:

The early internet’s biggest advantage was its ability to aggregate previously disparate supply and demand at scale, while eliminating inefficient middlemen. The businesses that won in this era built marketplaces and platforms where more users made the platform better for everyone, creating self-sustaining network effects.

Why These Models Were Powerful:

• Network Effects Created High Retention: Once a marketplace reached critical mass, it was difficult to compete against.
• Asset-Light Scalability: These companies didn’t own inventory; they simply connected buyers and sellers at scale — a significant advantage over their predecessors.
• Data Scale: New targeting and ad revenue channels became immensely powerful
• Lower Costs → Lower Prices: By removing middlemen, they passed savings to consumers.

Weaknesses & Evolutionary Gaps:

• Static Experiences: Even with aggregation, the experience was still unilateral and required active intervention from consumers.
• Limited Real-Time Capabilities: Booking, shopping, and search were efficient, but they didn’t happen in real time—that would come with mobile.

2nd Generation: The Mobile Era (2010-2020) – Real-Time, Personalization & On-Demand Models

Core Business Model Advantages: Intermittent-Frequent Engagement, Instant Accessibility, and Personalization

How It Worked:

If the internet era was about removing friction in access, the mobile era was about removing friction in time—bringing services directly to users, making transactions instant, fueling new sticky behaviors, and allowing increasingly personalized experiences.

Why These Models Were Powerful:

• Instant & On-Demand: Products and services were untethered from locations and desktops, and consumers could book, buy, or stream instantly from anywhere.
• [Light] Personalization at Scale: Algorithmically-driven content recommendations replaced static user searches.
• New Habit Formation: New sticky behaviors emerged from viral loops, gamification, and instantaneity, making new interfaces indispensable. 

Weaknesses & Evolutionary Gaps:

• Still Required Consumer Action: Even with personalized recommendations, users still had to actively engage with platforms.
• Scaling Costs Increased: Unlike the first wave, many of the most significant companies, such as Uber and Airbnb, had physical constraints (cars, drivers, homes).

3rd Generation: The AI Era (2022-Present) – Automation, Intelligence & Continuous Learning

Core Business Model Advantages: Continuous Learning, Next-Level Personalization, and Proactive Automation

How It Works:

Today’s new platform shift moves beyond real-time engagement to automated decision-making and proactive experiences. Instead of users searching, browsing, or even tapping, AI-native platforms preemptively offer insights and actions, eliminating decision fatigue and making interactions effortless. The continuous learning makes the experience so personalized, switch costs ultimately become almost impossibly high through their accrued context.

Why These Models Are More Powerful Than Mobile:

• Gen AI Continuously Improves: Unlike previous models, AI platforms get smarter over time, creating self-reinforcing retention loops.
• Decision Fatigue Is Relieved: Instead of researching and manual comparison, users get intelligent prompts.
• Frictionless & Proactive: AI personalizes and predicts actions before users request them.

Ultimately, Each Era’s Winners are Masters of Core Business Model Advantages of Their Time

The most successful companies don’t just offer new technology; they manipulate the business model advantages to have an unfair edge. As we move into an AI-driven future, the dominant companies will be those leveraging self-learning systems, automation, and predictive intelligence to reduce friction to zero and make the consumer experience effortless.

PART III: The Engineers, The Designers, and The Architects: Who Wins in Each Era?

Each wave of technological transformation has required leaders with different core skills to stand out. Early on, technical ability was the primary differentiator, but as tech stacks have evolved and lowered the barrier to entry, the key advantage is shifting toward vision, orchestration, and consumer insight rather than raw technical execution.

Let’s break it down across each major transformation wave:

1st Generation: The Internet Era (1995-2010) – Technical Proficiency & Digital Infrastructure

Key Skill: Engineering & Software Development

At the dawn of the internet, the most valuable skill was knowing how to build software and infrastructure. Because the early internet companies—Amazon, Google, PayPal, and eBay—were built to solve fundamentally technical problems:

• How do you efficiently and safely process payments online? (PayPal)
• How do you serve search results at a massive scale? (Google)
• How do you build a vast and secure online commerce and logistics platform? (Amazon)

At this stage, the ability to code and build new internet-based applications was the rare, high-value skill. The winners were those who could create the technology that made the internet more complex and usable at scale.

What made talent stand out in this era?

• Deep technical skills in programming, database management, and web architecture.
• Ability to build from scratch—everything had to be custom because cloud services, APIs, and third-party tools didn’t yet exist.
• First-mover technical advantage—being able to write efficient code gave companies an edge because fewer people had this capability.

Why This Evolved:

By the late 2000s, infrastructure like AWS, Stripe, and open-source tools abstracted away much of the hard engineering work, allowing new businesses to build without deep technical expertise.

2nd Generation: The Mobile Era (2010-2020) – UX, Growth, and Personalization at Scale

Key Skill: Mobile Product Design & Data-Driven Growth

Once mobile and cloud computing matured, the playing field shifted. With AWS providing scalable servers, Stripe handling payments, and social media allowing for audience-building, the key skill shifted from building infrastructure to designing instantaneous, sticky user experiences.

Winning in this era wasn’t necessarily about who had the best engineers—it was about who could create the most engaging, habit-forming mobile experience.

What made talent stand out in this era?

• Mobile-first UX & design thinking – Companies like Uber, Airbnb, and Instagram succeeded because they made transactions real-time and seamless.
• Data-driven growth & behavioral psychology – The best product teams mastered A/B testing, retention loops, and viral mechanics (e.g., Instagram’s social sharing, TikTok’s algorithm) to turn engagement into a daily (or hourly) habit.
• Monetization mechanics – The mobile economy relied on in-app purchases, ads, and subscriptions (e.g., Netflix’s shift to streaming, TikTok’s ad revenue).

Why This Evolved:

As mobile UX patterns became standardized, companies that simply optimized for engagement and retention saw diminishing returns. The next wave would require even more intelligence and automation to stand out.

3rd Generation: The AI Era (2020-Present) – Product Vision, Orchestration & Continuous Learning

Key Skill: Consumer Insight, AI Orchestration, Excellence in Delivery and Distribution

Now, in the AI-driven era, technical execution is no longer the primary differentiator. AI tools like Scale AI Copilot, Cursor, and off-the-shelf APIs (such as OpenAI, Anthropic) are democratizing access to technical capabilities. Engineers are still critical, but they’re now super-powered by AI, able to ship faster with far less resourcing than before.

This shifts the core competitive advantage from “Who can build it?” to “Who can assemble and refine it best?”

What makes talent stand out in this era?

• Visionary product design & consumer insight – When raw engineering skills are abstracted, the most valuable skill shifts to knowing how to assemble the best AI-driven tools into an intuitive, delightful, and indispensable experience. 
• Data strategy & continuous learning – AI-powered companies need to collect and refine user data in a way that gives the business an unfair advantage over time, by creating stickier and more intelligent products. 
• Consumer trust & AI-human interaction – As AI tools become more prevalent, the winners will create trustworthy, transparent, and ethical AI systems that users feel intentional about engaging with.
• Distribution excellence — While distribution is always pinnacle, commoditized tech stacks mean copying interfaces and ideas is easier than ever. Distribution edges are now uniquely powerful, both in getting ahead of competition and fueling data accumulation and continuous learning loops that lock consumers in.

Why This Is the Most Valuable Skillset Today:

Architecting is king. The real competitive advantage comes from integrating AI models into world-class user experiences that are uniquely poised to win through continuous learning.

What This Means for Today’s Builders & Entrepreneurs:

In the 90s, software engineers were the kings. If you could build a website, an e-commerce platform, or a search engine, you could change the world.

By the 2010s, product designers and growth marketers became the most valuable players. If you could create an intuitive mobile experience, optimize for engagement and monetization at scale, you would be in a prime position.

Now, in the AI age, the coveted skill is product vision and orchestration. The best AI-native companies will not just use AI as a feature, but as the core of their business model, to create entirely new consumer experiences that get better with time.

PART IV: User Adoption Challenges and Pathways to Overcoming Them

AI-driven experiences promise a frictionless future, but only when we solve for the user adoption hurdles that businesses will inevitably navigate. These challenges stem from trust, economic factors, and behavioral inertia, and overcoming them will require a combination of education, transparency, human-AI collaboration, and regulatory adaptation.

Key Challenges in User Adoption

Trust and Transparency Concerns:

Users often struggle to trust AI-driven decisions, particularly in high-stakes areas such as finance, healthcare, and personal data security, without fully understanding how AI reaches conclusions. 

Example: AI-powered investment platforms may outperform human advisors on paper, but consumers might hesitate to trust an algorithm with their finances unless they understand its decision-making logic.

Loss of Control and Human Autonomy:

Many consumers are accustomed to being active participants in decision-making and routine processes. The idea of AI fulfilling purchasing, financial, or even lifestyle decisions on their behalf can feel unsettling.

Example: AI-powered shopping assistants might automatically reorder essentials, but users may resist if they feel like they’re losing control over what they buy and when.

Job Displacement Fears and Economic Disruption:

AI automation threatens traditional roles in multiple industries, leading to serious concerns about job security and economic shifts. Adoption may be resisted not just by consumers, but industries that feel their value is eroded by automation.

Example: AI-generated content platforms may face resistance from traditional creative industries, where professionals worry about being replaced.

Bias and Ethical Considerations:

AI models are only as good as the data they are trained on, and biases in AI outputs have led to concerns around imposed influence and discrimination. Users may hesitate to trust AI-driven decisions if they perceive unfairness.

Example: AI-driven hiring tools have faced scrutiny for replicating biases in historical hiring patterns, leading to pushback from HR professionals and regulatory bodies.

Regulatory Uncertainty and Compliance Barriers:

AI adoption is often slowed by unclear or evolving regulations around privacy, data usage, and liability. Navigating this uncertain legal landscape can create friction for users and businesses alike.

Example: GDPR and other data protection laws have already forced AI companies to rethink their data collection and processing methods.

Pathways to Overcoming These Challenges

Enhancing AI Explainability and Transparency:

AI models must move beyond black-box operations to explainable AI, where users can understand why AI makes certain recommendations or decisions to build confidence.

Example: AI-powered finance platforms can introduce explainer dashboards that show the reasoning behind investment decisions.

Balancing AI Automation with Human Oversight:

AI should augment, not replace, human decision-making. Offering a hybrid human-in-the-loop model where AI assists rather than fully automates will ease adoption.

Example: AI-driven healthcare diagnostics can provide recommendations but still require a final approval from a human doctor, reinforcing trust.

Regulatory Alignment and Ethical AI Standards:

AI companies must proactively engage with policymakers to create ethical guidelines that ensure fairness, transparency, and security. Establishing AI ethics boards or third-party audits can reinforce credibility.

Example: Anthropic’s Crowd-Sourced AI Constitution served as a framework for understanding AI’s potentially detrimental impacts based on externally validated inputs and parameters. 

Addressing Economic Disruptions with Workforce Adaptation:

Many economists predict that AI will create more jobs than it will replace, but still, the depth of AI-related job displacement is unknown. Regulators, businesses, and educators must collaborate on initiatives to prepare workers for AI-enhanced industries.

Example: AI tools that automate coding can mean more products shipped faster, creating higher demand for product and growth management.

AI adoption will not be immediate or universal—it will require trust, transitionary stages, and clear value creation for consumers. 

Ultimately, The Future Belongs to the Architects of Experience

If the internet era was about access, and the mobile era was about instantaneity, the Gen-AI era is about effortlessness—removing human inefficiency, making transactions invisible, and proactively anticipating needs.

With each platform shift, new businesses have replaced outdated, less efficient incumbents by better serving the modern consumer and mastering the business model innate to the new era. The winners of this new era will build self-evolving systems that get smarter with every interaction and create value for consumers before they even realize they need it. 

That means AI is more than just a technology—it is the business model itself.

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