Applied AI Dominance: Why Vertical Solutions Beat Foundation Models in 2025

The foundation model era is over. The applied AI economy is now. For AI founders and technical leaders, this shift from building models to building solutions determines whether your venture becomes a category leader or a commodity.

The artificial intelligence landscape is no longer a distant frontier; it's the ground beneath our feet, rapidly reshaping every industry. For founders and tech leaders building in this dynamic space, the strategic playbook is evolving at an unprecedented pace. Insights from leading voices in the AI revolution, including those shared at pivotal gatherings like Sequoia Capital's AI Ascent, offer a clear directive: the era of foundational model worship is waning, and the age of applied, vertical AI and intelligent agents is dawning.

If you're charting a course for an AI venture today, the lessons emerging from the front lines are invaluable. This isn't just about technology; it's about building enduring companies in a world being fundamentally rewritten by intelligence itself.

The Macro Shift: AI Isn't a Market, It's the Market-Eater

The consensus is clear: AI represents an opportunity dwarfing previous tech waves like cloud and mobile in speed and scope. It's not a niche; AI is becoming a fundamental layer across all markets - software, services, labor, and infrastructure. As Sequoia Capital has highlighted, while 2023 was a year of frenzied exploration, 2024 and beyond are about converting AI's "primordial soup" of potential into tangible, impactful solutions.

The most significant strategic understanding is that value is migrating to the application layer. While foundational models are becoming increasingly powerful and accessible, the real winners will be those who build indispensable tools that solve specific user problems effectively. This means a shift from a model-forward approach to a customer-back strategy. Don't start with a model and search for a problem; identify a critical job-to-be-done and leverage AI to solve it completely.

Building an AI-Native Company: The New Rules of Engagement

The playbook for constructing a successful AI company in 2025 blends timeless business principles with AI-specific strategies:

1. Embrace Vertical Depth: As foundational models commoditize and move up the stack, the path to differentiation lies in going narrow and deep. Compete by owning a specific workflow or serving a particular persona more comprehensively than a general-purpose model ever could. OpenAI itself encourages startups to leverage its APIs to tackle niche, high-friction problems that demand domain expertise and tight UX control.

2. Opinionated Products Win: In a world where users are still discovering AI's capabilities, it's the founder's role to have a strong vision. Build opinionated products that guide the user, rather than presenting a blank canvas and asking, "What do you want?" The best AI products feel magical and intuitive, not like a complex toolkit.

3. The Value Ladder: Tool → Co-pilot → Autopilot: Successful AI applications often evolve. They might start as a useful tool, progress to a co-pilot augmenting human capabilities, and ultimately aim for an autopilot state, handling tasks autonomously. Understanding this progression helps in product roadmapping and value delivery.

4. Data Flywheels as Moats: While 95% of company building remains about team, execution, and product, the AI-specific 5% - particularly the data flywheel - becomes critical for defensibility at scale. If your users' behavior and interactions with the product don't continuously improve the underlying AI and, consequently, the user experience, your moat is shallow. True moats are built on proprietary usage data that creates a performance lift, making your solution increasingly difficult to replicate.

5. Focus on Real Revenue, Not Hype: The market is maturing. Traction is measured by adoption, retention, and tangible behavior change, not just "vibe revenue" or buzz. While early gross margins might be impacted by token costs, these are generally decreasing, and margins tend to improve with scale.

6. UX is King, Workflow is Your Kingdom: The underlying AI model is secondary if the product experience is poor. Startups often win on superior user experience and by owning the end-to-end workflow for their users. Don't just build tools; build complete outcomes.

The Agent Economy: AI's Next Platform Shift

The conversation is rapidly moving beyond chatbots. The next major platform shift is towards AI agents: systems that can coordinate, reason, and act to execute complex tasks. This "Agent Era" envisions swarms of specialized agents collaborating, much like human teams, to get work done. Instead of prompting a single AI, users will orchestrate dozens.

However, several blockers remain before this vision is fully realized:

• Memory: Enabling agents with persistent personal and long-term memory for context.

• Protocols: Establishing standardized ways for agents to communicate and collaborate.

• Security & Trust: Ensuring the reliability, identity, and auditability of autonomous agents.

The company that cracks these challenges could effectively create the "AI operating system."

Building Trustworthy and Effective Agents: Lessons from the Trenches

Founders at the forefront of agent development offer critical insights:

• Ramp's Approach - Agents Interacting with UIs: A primary reason many AI agents fail is their inability to complete entire workflows. They often get stuck after one step due to incomplete API access to an application's full capabilities. Ramp's innovative solution, as shared at events like AI Ascent, involves enabling agents to interact directly with user interfaces (UIs) - essentially, a headless browser "clicks" around the frontend as a human would. This allows agents to leverage full feature coverage from day one without requiring a product rebuild or new infrastructure. Trust is built by allowing users to see and pause the agent's actions.

• Langchain's Vision - Ambient, Event-Triggered AI with Human Oversight: Langchain champions the concept of ambient agents that operate in the background, responding to signals and events rather than direct prompts. A key innovation is the "Agent Inbox," a command center for human oversight. This underscores a critical principle: human-in-the-loop is not optional for complex or sensitive tasks. Trust is cultivated through user control, comprehensive logs, and the ability to audit and reverse agent actions. The ultimate goal is agents that can self-improve based on their interactions within defined workflows.

The Future is Vertical, Fast, and Trust-Centric

For founders navigating this landscape, the message from industry leaders is consistent:

• Don't Fight the Giants on Their Turf: Attempting to build foundational models to compete with entities like OpenAI or Anthropic is a losing proposition for most startups. Instead, leverage their APIs as the "HTTP for intelligence" and build on top.

• Speed is a Key Differentiator: In many verticals, there's a vacuum of AI-native solutions. The ability to ship quickly and iterate can create a significant first-mover advantage. Modern distribution channels mean that if a product truly works and solves a problem, it can scale rapidly.

• Design for Trust: As AI systems become more autonomous, user trust is paramount. This isn't just about security; it's a core UX principle. If users can't see, understand, or control what AI agents are doing, they will abandon the product.

• The Next Frontiers - Voice, Code, and Robotics: The evolution doesn't stop at text-based agents. The future points towards agents that can understand and generate voice, write and execute code, and even interact with the physical world through robotics, often trained extensively in simulation.

We are moving beyond managing code to managing systems that think. It's no longer just about being early to AI; it's about being right - right about the customer problem, right about the vertical focus, and right about building an experience that is both magical and trustworthy. The AI ascent is steep, but for those who build with vision and precision, the summit offers unprecedented opportunities.

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Written by Dr Hernani Costa | Powered by Core Ventures

Originally published at First AI Movers.

Technology is easy. Mapping it to P&L is hard. At First AI Movers, we don't just write code; we build the 'Executive Nervous System' for EU SMEs.

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