The AI Agent Gold Rush: Why the Biggest Threat Isn’t Hype—but a Missing Moat
A lengthy thought on where we are and where we are headed
There’s a lot of buzz right now about AI agents. Venture capitalists are throwing money at startups that promise to revolutionize work with autonomous “agents” that do our bidding with a few words of instruction. On the surface, it’s a modern gold rush reminiscent of the mobile app boom of the early 2010s. But if history has taught us anything, it’s that every gold rush comes with its own set of pitfalls. And here’s the contrarian truth: while everyone is dazzled by the transformative promise of AI, the real challenge isn’t the technology—it’s building a defensible moat that can stand up to copycats, platform gatekeepers, and shifting user expectations.
I learned early on in my career—back when I co-founded Swoopt, the very first real‑money daily fantasy sports app on the iOS App Store back in 2013—that disruptive ideas require not only an innovative product but also a rock‑solid strategy for long‑term differentiation. The lessons from the mobile era remain remarkably relevant today, even as we embark on the new frontier of AI agents. Let’s unpack how the evolution of design, business strategy, and the contrarian insights about defensibility can help product leaders navigate the AI agent gold rush.
I. From Mobile to AI Agents: A Revolution in Design & Product Thinking
When smartphones burst onto the scene, they didn’t just change our devices—they transformed the very way we interact with technology. Mobile design forced product teams to rethink every aspect of user experience. Now, as AI agents begin to shoulder more of the heavy lifting, a similar revolution is taking shape in how we design products.
A. The Mobile Design Transformation
Back in the early days of mobile apps, designers were grappling with a fundamental challenge: how to create rich, engaging experiences on a small, touch‑driven screen. The desktop paradigms didn’t translate well. We had to invent new interaction patterns—swipe gestures, pinch‑to‑zoom, and minimalist interfaces that cut out clutter and focused on what mattered most.
Take, for example, the evolution from skeuomorphic designs (those delightful, almost tangible interfaces that mimicked real‑world objects) to the flat, minimalist aesthetics that define modern mobile apps. This wasn’t just about looking pretty; it was about creating an interface that worked seamlessly on the go. It was about reducing friction so that every tap, swipe, and pinch led the user effortlessly from point A to point B. Over time, features like native notifications, context‑aware interfaces, and rapid A/B testing cycles became the norm, all in service of one goal: making the experience as intuitive as possible.
B. Parallels in AI Agent Product Design
Now, consider AI agents. Their promise isn’t merely to serve up information on demand—it’s to handle tasks, coordinate workflows, and even interact with other software on your behalf. This shifts the design challenge from “How do we make this app look good on a small screen?” to “How do we design an experience where the technology works almost invisibly in the background, yet remains trustworthy and responsive?”
Conversational Interfaces & Invisible UIs:
The first major difference is that AI agents often communicate in natural language. Instead of clicking through menus, users might simply type or speak a command like, “Schedule my meetings for tomorrow.” The user interface in these cases is not a traditional dashboard but a conversation. This forces us to think in terms of dialogue design—anticipating user intent, managing follow‑up questions, and providing transparency through activity logs. Designers are now tasked with creating an “invisible interface” that lets the AI handle complexity while keeping the user in the loop. This is very much like how early mobile apps minimized distractions, focusing only on what was necessary for the user’s task.
New Mental Models & Agent‑to‑Agent Interaction:
Another fascinating shift is that sometimes the AI agent becomes not just a tool for the user but a participant in a larger ecosystem. Imagine an AI that can navigate a shopping website, make API calls, or even coordinate with another AI agent in a partner application. In designing for these interactions, we’re forced to consider “Agent Experience” (AX) alongside traditional user experience (UX). Just as mobile developers had to ensure that their apps could speak to other apps via URL schemes or APIs, product teams today must design for seamless AI-to-AI communication. The aim is to create a consistent, machine‑readable experience that functions as reliably for an AI as it does for a human.
Trust, Safety, and Ethical Design:
One of the most pressing challenges in both mobile and AI design is establishing trust. Mobile apps had to confront issues like data privacy and secure permissions (think location access or contacts). AI agents, however, are operating on an entirely different level—they might book flights, send emails, or even make financial decisions. This magnifies the need for robust ethical safeguards. Designers are implementing permission gating, clear opt‑out options, and explainability features that allow users to understand the rationale behind an AI’s decisions. It’s a modern echo of the early mobile days when a good App Store rating or a transparent privacy policy could make or break user adoption.
C. A Lesson From Swoopt
During my time as co‑founder at Swoopt, we were at the bleeding edge of mobile innovation. In 2013, we were among the first to harness the potential of iOS for a real‑money game—a bold experiment that taught me how crucial it was to design for the medium rather than simply porting over desktop ideas. At Swoopt, we learned that success hinged on an intimate understanding of the platform’s strengths and limitations. Today, as product leaders consider integrating AI agents, the same principle applies: build your product around what the new technology does best rather than trying to retrofit old models. Embrace the conversational, invisible, and ethically sound design paradigms that AI enables, and you’ll be well on your way to creating a product that isn’t just novel, but truly indispensable.
II. Business Strategies: Learning from the Mobile Era
The mobile revolution wasn’t only a design renaissance—it was also a master class in business strategy. In the mobile era, successful startups weren’t those that simply built an app; they built an ecosystem around a native experience, leveraged network effects, and scaled rapidly to capture market share before the big players could react. As AI agent startups emerge, many of these strategies remain as relevant as ever.
A. Native Experiences and Product-Market Fit
In the mobile world, one of the key success factors was the creation of a truly native experience. Rather than trying to shoehorn a desktop website into a mobile screen, companies like Instagram and Snapchat built their products from the ground up with mobile in mind. They exploited the unique features of smartphones—the camera, touch gestures, GPS, and push notifications—to deliver experiences that were both engaging and contextually relevant. This approach wasn’t just about aesthetics; it was about rethinking the entire user journey. For example, Instagram’s focus on instant photo sharing, with a simple, intuitive interface, quickly amassed millions of users and even attracted a $1B acquisition by Facebook (Instagram: What It Is, Its History, and How the Popular App Works).
For AI agents, the lesson is similar: Don’t just add an “AI feature” on top of an existing product. Instead, design your product from the ground up to take full advantage of AI’s capabilities. Whether it’s automating tedious workflows or providing natural language assistance, your product should leverage the inherent strengths of AI to solve real user problems. This means understanding what tasks can be truly automated and building an experience that minimizes friction—much like the mobile apps that simplified our lives with a swipe or a tap.
B. Network Effects and the Power of Integration
Mobile startups like WhatsApp and Facebook demonstrated the power of network effects. WhatsApp, for instance, grew its user base rapidly by tapping into the natural social networks of mobile users—people’s phone contacts. This kind of viral growth was less about flashy marketing and more about building a product that became indispensable simply by being where users already were. Once a critical mass was reached, the product’s value grew exponentially, creating a defensible moat against competitors.
In the AI agent space, network effects can come in different forms: Data network effects and integration network effects. An AI agent that learns from every interaction becomes smarter and more personalized over time—a feedback loop that can be a powerful competitive advantage. Moreover, integration with popular platforms (think Gmail, Slack, or even niche enterprise software) can drive rapid adoption. The key is to make your agent so indispensable that switching costs become high. For example, an AI scheduling agent that integrates deeply with a user’s calendar and email systems will improve as it learns user habits, and users will be less likely to migrate to a competitor.
C. Blitzscaling and the Race to Dominate
The mobile era was marked by a “get big fast” mentality. Startups were willing to forgo immediate monetization in favor of rapid user acquisition. Instagram, Snapchat, and Uber all demonstrated that capturing market share quickly could lead to outsized returns—even if the initial business model was unproven. The rationale was clear: in a fast-moving market, establishing a dominant position was worth the risk.
For AI agent startups, a similar approach may be necessary. The technology is evolving rapidly, and first movers may have a significant advantage in capturing market share and user data. However, this also means higher upfront costs. Training sophisticated AI models, securing proprietary data, and integrating with multiple platforms can require significant capital. Venture capitalists today are willing to invest heavily in these areas, betting that the winners will be those that achieve scale before the big tech incumbents can react (Part I: The future of AI is vertical - Bessemer Venture Partners). Yet, as we learned in the mobile era, rapid scaling is a double-edged sword. Without a robust strategy for long-term differentiation, even the fastest-growing startup can find itself vulnerable to competitive pressures.
D. Strategic Adaptation: Incumbents vs. Innovators
Mobile’s gold rush wasn’t without its casualties. Many startups with brilliant ideas were eventually swallowed up by larger companies or edged out by more agile competitors. One of the key strategies for survival was adaptation—either by integrating with larger platforms or by carving out a niche that the big players overlooked. Facebook’s own pivot to mobile is a prime example; when it became clear that the future was in the smartphone, the company rapidly reengineered its product and business model to embrace the shift (Facebook’s Zuckerberg says mobile first priority | Reuters).
For AI agent startups, the lesson is clear: choose your battles wisely. Rather than directly challenging the tech giants on every front, look for areas where you can build a vertical, specialized product that addresses a real pain point. Whether it’s a legal AI agent that handles document drafting (#099: AI Agent App Stores, EvenUp Vertical AI Valued At $1B+, Growth Rate Impact On EV) or an AI agent designed for niche enterprise workflows, focusing on a specific domain can help you build a moat that is hard for incumbents to replicate overnight.
III. The Contrarian View: Defensibility, Moats, and Platform Pitfalls
Here’s where the gold rush gets tricky. Amid all the excitement, there’s a growing contrarian narrative among investors and product leaders alike: many AI agent startups risk building a house of cards if they fail to create a defensible moat. History is littered with examples from the mobile era where rapid growth was followed by stagnation, or worse—being cannibalized by larger platforms.
A. The Mirage of the Moat
One of the most persistent criticisms of current AI agent startups is that they often look like “wrappers” around the same underlying technology—typically large language models like OpenAI’s GPT‑4. In many cases, these startups differentiate themselves only by applying a thin layer of domain‑specific branding or a marginal tweak to the user interface. The underlying engine, however, remains largely the same across competitors.
Critics argue that such products risk becoming a free market research exercise for the incumbents. If your competitive edge is solely based on access to a powerful AI model, what happens when the model provider—say, OpenAI—decides to integrate that functionality directly into its own offerings or license it more broadly? History provides a cautionary tale: during the mobile boom, once a killer app’s concept was proven, it was only a matter of time before the tech giants either copied the feature or absorbed the startup into their ecosystem.
So how can you build a real moat in the AI era?
The answer lies in vertical specialization and proprietary data. By tailoring your AI agent to a specific industry or workflow—where you can accumulate domain‑specific knowledge and exclusive datasets—you create barriers that generic models can’t easily overcome. For example, a legal AI agent that learns from thousands of confidential legal documents develops a level of expertise that a general‑purpose model simply cannot match without access to the same data. This is analogous to how mobile apps like Waze or Yelp built their competitive advantage through unique, user‑generated content (Defensibility for AI Startups – Kenneth Lange).
B. Platform Constraints: The Modern Gatekeepers
In the mobile era, startups quickly learned that Apple and Google weren’t just platforms—they were powerful gatekeepers. Their control over app distribution, the rules of the ecosystem, and even revenue-sharing models (like the notorious 30% cut) meant that even the most innovative apps could find themselves at the mercy of a single policy change. This created a dynamic where being on the “right” platform was as important as the product itself.
Today, AI agent startups face a similar conundrum. Most rely on a handful of AI model providers, and many of their integrations depend on large, established ecosystems—whether it’s an API integration with Gmail or a plugin in Microsoft 365. If these platform owners decide to build their own AI agents or impose new constraints, startups can find themselves suddenly squeezed out of the market. For instance, if OpenAI were to prioritize its own AI agent products or alter its API pricing, startups built on its technology might struggle to maintain their competitive edge.
The lesson for product leaders: Diversify your dependencies.
Plan for a future where your product isn’t solely at the mercy of one provider or ecosystem. Consider hybrid models that can pivot between different AI backends or develop contingency plans for shifts in platform policy. In the mobile world, this might have meant supporting both iOS and Android robustly or even developing progressive web apps to bypass app store restrictions. For AI, it might involve investing in proprietary training data or exploring open‑source models as a fallback.
C. Differentiation Beyond the Underlying Tech
There’s a risk that AI agents become seen as just another flashy feature rather than a transformative product. In the mobile era, many apps enjoyed an initial burst of downloads fueled by hype—but without sustained value, they quickly faded away. Similarly, an AI agent that doesn’t offer a clear, 10x improvement over traditional methods may struggle to retain users. It’s not enough to simply show off impressive demos or boast about cutting-edge algorithms; the product must solve a real problem better than any alternative.
This is where design and integration become critical. A product that successfully marries AI with a seamless, intuitive user experience can build lasting loyalty. Think of it like this: the best mobile apps weren’t just technically superior—they were delightful to use, solving everyday problems with elegance and simplicity. For AI agents, the goal should be similar: build a product that users not only rely on but also love, because it takes care of the mundane so they can focus on what truly matters.
D. Economic and Ethical Pitfalls
Finally, there are the less glamorous—but no less important—concerns of unit economics and ethical responsibility. AI agents, particularly those operating at scale, can face significant cost pressures. Every API call, every model inference, carries a cost. If the savings generated for a user don’t outweigh the operational expenses, the business model may not be sustainable. Mobile apps faced similar challenges before ad networks and in‑app purchase models matured enough to support “free” apps. For AI, the hope is that compute costs will decline or that enterprise clients will be willing to pay premium prices for meaningful automation.
Moreover, as AI agents take on roles that involve decision‑making or sensitive tasks, the ethical stakes are raised. What happens if an agent makes an error that leads to financial loss, or worse, damages a brand’s reputation? Product leaders must invest in robust safeguards, transparent accountability measures, and clear user consent protocols. These aren’t just technical challenges—they’re business challenges that can make or break your product’s long‑term viability.
IV. Charting a Course Through the AI Gold Rush
So, what’s the roadmap for product leaders navigating this AI revolution? The parallels to the mobile era are clear, but so are the unique challenges of the AI landscape. Here are some strategic imperatives distilled from the lessons of both eras:
1. Build Native, AI‑First Products
Rather than retrofitting AI features onto legacy systems, design products from the ground up with AI at their core. Embrace the conversational, invisible interfaces that set AI apart from traditional software. This approach is akin to the mobile‑first mindset that propelled apps like Instagram and Snapchat to success.
2. Specialize to Create Defensible Moats
Focus on verticals where you can accumulate exclusive data and domain expertise. Whether it’s legal, healthcare, or another specialized field, tailor your AI agent so that it solves a specific problem exceptionally well. This specialization will serve as your moat in a market where many competitors share the same underlying technology.
3. Prepare for Platform Dynamics
Diversify your dependencies and build resilience against shifts in platform policies. Whether it’s having multiple AI model providers or creating integration layers that work across ecosystems, don’t put all your eggs in one basket. Learn from the mobile world’s struggles with gatekeepers like Apple and Google, and plan your architecture accordingly.
4. Prioritize Trust and Transparency
Design your product not just for efficiency but for trust. Implement robust ethical guidelines, clear user permissions, and transparent explanations for the decisions your AI makes. Trust isn’t built overnight—it’s earned through consistent, reliable performance and open communication with your users.
5. Scale Wisely and Stay Agile
Rapid growth can be a double‑edged sword. Focus on achieving product‑market fit before scaling aggressively, and remain agile enough to pivot when necessary. As we saw in the mobile era, the winners were those who combined visionary thinking with disciplined execution, constantly iterating based on user feedback and market dynamics.
V. Concluding Thoughts: The Future Isn’t Written in Code—It’s Written in Strategy
The AI agent gold rush promises to reshape our work, our communication, and even our daily lives. The excitement is real, and the opportunity enormous. But if history has taught us anything—whether through the lessons of the mobile revolution or my own experiences at Swoopt—it’s that technology alone doesn’t guarantee success. The real differentiator is the strategy: the ability to design products that delight users, build sustainable competitive advantages, and navigate the complex dynamics of platform ecosystems.
Product leaders today stand at a crossroads. On one path lies the dazzling promise of AI—a future where machines handle the mundane, allowing humans to focus on creativity and strategic thinking. On the other lies a cautionary tale: a market flooded with AI agents that, while impressive on paper, lack the defensible moats needed to survive in a competitive landscape. The contrarian insight here is both simple and profound: don’t be seduced by the hype. Instead, focus on building products that solve real problems, integrate seamlessly into users’ lives, and, above all, are designed to stand the test of time.
By learning from the mobile era—a time when design innovation, native experiences, and rapid adaptation redefined the tech landscape—we can chart a course through the AI revolution with our eyes wide open. The strategies that propelled mobile apps to global dominance are not relics of the past; they are blueprints for the future. And if there’s one thing I learned during my time at Swoopt, it’s that true innovation isn’t just about riding a wave—it’s about building the boat that can weather the storm.
So, as you contemplate your next move in the world of AI agents, ask yourself: How will you build a product that not only dazzles with technological prowess but also creates a lasting, defensible advantage? How will you ensure that your innovation isn’t just a flash in the pan, but a cornerstone of a sustainable, scalable business?
The AI agent gold rush is here, and the stakes are higher than ever. With thoughtful design, strategic focus, and a relentless commitment to user value, the winners will be those who look beyond the hype and build something truly transformative. The future is being written now—not just in code, but in the strategic decisions we make today.
Key Takeaways for Product Leaders
Embrace an AI‑First Mindset:
Design your products from the ground up with AI at their core. Build conversational, invisible interfaces that harness the unique strengths of autonomous agents.
Create Defensible Moats:
Focus on vertical specialization and accumulate proprietary data to build a competitive advantage that’s difficult for competitors to replicate.
Anticipate Platform Constraints:
Diversify your dependencies, prepare for shifts in platform policies, and design for resilience against the gatekeeping tendencies of major ecosystems.
Invest in Trust and Transparency:
Prioritize ethical design, clear user permissions, and transparent AI behavior. Trust is the currency of the digital age.
Scale With Agility:
Rapid growth is important, but it must be tempered with disciplined execution and continuous iteration. Learn from the mobile era’s successes and failures to guide your strategy.
As product leaders, the onus is on us to not only harness the transformative potential of AI agents but to do so with a clear-eyed understanding of the challenges ahead. By drawing on the hard-earned lessons of the mobile revolution—and by integrating them with the unique opportunities and risks of AI—we can build products that aren’t just trendy, but transformative.
The gold rush may be glittering with promise, but true value lies in crafting a product that can weather the test of time. Let’s build that future—one thoughtful, well‑designed, and resilient AI agent at a time.
Things referenced to write this:
• Instagram: What It Is, Its History, and How the Popular App Works
• Facebook’s Zuckerberg says mobile first priority | Reuters
• Part I: The future of AI is vertical - Bessemer Venture Partners
• #099: AI Agent App Stores, EvenUp Vertical AI Valued At $1B+, Growth Rate Impact On EV
• Defensibility for AI Startups – Kenneth Lange
By blending the art of design, the rigor of strategy, and a healthy dose of contrarian thinking, we can navigate this new frontier with both optimism and caution. The AI agent gold rush is underway—make sure your startup isn’t just a flash in the pan, but a legacy in the making.