Imagine a world where AI isn't just a tool, but a collaborator. A world where design isn't limited by constraints, but fueled by infinite possibilities.

In this new era of AI abundance, designers and developers must adapt to a rapidly changing landscape. By understanding the power of data infrastructure and the potential of AI agents, we can create innovative and user-centric experiences.

Data Infrastructure Is The New Gold Rush

Data is the new oil. Companies like Uber, ByteDance, and Tesla have recognized the power of data infrastructure to fuel innovation and growth.

Uber is a prime example of a company that has leveraged data to disrupt the transportation industry. By collecting vast amounts of data on rider behavior, driver availability, and traffic patterns, Uber has been able to optimize its operations and improve the user experience.

ByteDance, the parent company of TikTok, has also harnessed the power of data to create a global phenomenon. By analyzing user behavior on its platform, ByteDance has been able to develop highly addictive algorithms that keep users engaged for hours on end.

Tesla is not just a car company; it's an AI company. By collecting data from its fleet of vehicles, Tesla is able to continuously improve its self-driving technology. This data-driven approach has allowed Tesla to stay ahead of the competition and accelerate the development of autonomous vehicles.

As AI continues to evolve, vertical AI will become increasingly important. By focusing on specific domains, vertical AI models can achieve higher levels of performance and accuracy.

While LLMs are becoming increasingly powerful and accessible, they are also becoming commodities. Just this week, the emergence of DeepSeek, a Chinese LLM, sent shockwaves through the AI community. Developed for a fraction of the cost and time of ChatGPT (Allegegedly, DeepSeek cost $6 million and was built in 2 months, compared to ChatGPT's estimated $100 million and 3+ months of development), DeepSeek demonstrates that achieving comparable performance with significantly fewer resources is possible. Dario Amodei, CEO of Anthropic sees it differently, "DeepSeek produced a model close to the performance of US models 7-10 months older, for a good deal less cost (but not anywhere near the ratios people have suggested)". Regardless of the truth of the matter, we must look not at foundation models to differentiate our products, but rather at agent capabilities and the roles we give them.

The differentiators of AI agent-based experiences will not be in their foundational model or LLMs, but rather tuning AI agents to specific skills and capabilities and the data moats they are capable of leveraging to specialize and improve the features. It is these vertical AI agents that will be unique and not commodities in the economy.

The Rise of AI Agents

AI agents are specialized AI systems designed to perform specific tasks. From customer service chatbots to autonomous vehicles, AI agents are transforming industries.

As AI agents become more sophisticated, they will handle increasingly complex tasks, freeing up human workers to focus on higher-level activities. Designers and developers must work closely with AI researchers to ensure that these agents are user-friendly and ethical.

Designing for the AI Age

In the age of AI, human-centered design remains essential. By understanding user needs and preferences, designers can create hyper-specific AI-powered experiences that are both functional and delightful.

The key principles for designing in the AI age:

• Ethical AI: Ensure that the user and AI agent relationship is clear and is orchestrated responsibly.

• User-Centered Design: Prioritize user needs, experiences and communication preferences with transparency and empathy for their goals.

• Continuous Learning: Collect data to continuously improve feature capabilities.

• Collaboration: Design systems that natively allow humans and AI agents to collaborate on deliverables.

For details on how to get started building vertical AI agents, review my action chain framework to get started strategically planning these agents capabilities, hypothesis of use and the data needed to build your competitive moat.

A Healthier Future with AI

One of the biggest grand challenge areas that will benefit from AI tools and techniques is healthcare. As Shaan Puri has observed, "one of the best places to find ideas is in seeing what needs the wealthy do and bringing that experience to the mass market." Historically, only the wealthy could afford expensive analysis of healthcare tests. But, due to inexpensive sensors such as Whoop and Apple Watch, anyone can now get health data collection and analysis that only millionaires could afford a few short years ago.

One exciting example is the work of Bryan Johnson, who is optimizing his health and longevity. By tracking his biomarkers, experimenting with different interventions, and adjusting his regimen based on biomarker improvements, Johnson is pushing the boundaries of human performance.

Imagine bringing parts of this world-class health performance routine into a mass-market health experience. At the core, analyzing vast amounts of personal medical data, providing health recommendations rooted in scientific studies, and adjusting the course based on biomarker improvements.

AI agents can help scale this premium experience by scaling medical knowledge and insights. Further, this mass-market experience will become better than what any wealthy person could afford as data is gathered en masse. We will see an experience that is able to make specific recommendations, but is powered by the wisdom of the collective data that is gathered and analyzed.

This experience becomes one of the largest medical studies in history, giving it a large moat. This example is what I mean when I say vertical AI agents will have relevance and a protective moat around their businesses. As the vertical AI agent marketplace matures, increasingly hyper-specific AI will be created, tailoring to increasingly specific use cases.

So, what vertical AI agent will you build? The opportunity is ripe for those who can identify niche needs and leverage the power of data to create truly innovative solutions.

There’s a reason 400,000 professionals read this daily.

Join The AI Report, trusted by 400,000+ professionals at Google, Microsoft, and OpenAI. Get daily insights, tools, and strategies to master practical AI skills that drive results.

Sign up now for free and work smarter, not harder.

🧲 AI Agent Magnet

add 3x each week

A compelling visual explainer about how transformers work. As a visual learner, I like to actually see how something works even if an abstract process. This visualization really helps solidify the relationships of the architectural components inside a transformer.

Dario Amodei, CEO and co-founder of Anthropic makes the case for the need for export controls to keep American innovations from our adversaries and brings a clear-eyed view as a builder of similar, comparably capable models that the Deepseek work is not magnitudes better than models trained with similar techniques, in a similar timeframe as those at American companies.

I recently read this paper on a human-centered approach to develop an LLM finely tuned to operate a range of AI agent roles across the medical industry. This punctuates the opportunity to create vertical AI agents tailored to specific roles and tasks using specific data sets from the target industry to improve deliverables and, in this case, patient health outcomes.

💬 Suggestion box

A newsletter exploring the principles that will help us design AI agent experiences and startups that amplify human creativity.

Subscribe to join a community of designers and developers shaping purposeful AI agents.

New Referral Rewards

Until next time, keep innovating and stay curious!

Jason