Seeing AGI (5): Multi-Model and Multi-Agent is the Future

"The future of AI isn't about finding the one perfect model—it's about orchestrating the right combination of specialized models and agents to create experiences that no single system could ever deliver alone."

In my previous four articles, I've shared my journey witnessing AGI's arrival, adapting to it, and discovering how it transforms not just what we do, but how work feels. Today, I want to address a fundamental debate in the AI community: there are currently two schools of thought about the future. One believes in "one model to rule them all"—a single superintelligent system that can handle everything. The other believes in multi-model, multi-agent architectures. As one of the few companies that has built a general super agent and extensively tested thousands of AI products, I want to share why I believe the latter approach is the path forward.

As strategic partners of OpenAI, Anthropic, and other leading AI companies, we have some unique insights into this debate. Our partnerships give me deep, hands-on experience with the most advanced models from each major AI lab. Through building AI systems that serve millions of users and extensively testing virtually every significant AI model in production even before their production, I've reached what might be an unpopular conclusion: the "one model to rule them all" approach only sounds appealing in theory.

The reality is that combining the unique strengths of different specialized models with purpose-built agents will be the key to delivering truly exceptional user experiences.

I know this challenges some people's opinion. But before you dismiss this perspective, let me walk you through the reasoning that led me here.

Multi-Model is the Future

Let me start with a reality check that every AI practitioner knows: no single model right now excels at everything.

Through our partnerships with leading model teams, we have extensive experience with each major AI system's strengths and limitations. OpenAI excels at deep research and creative writing tasks. Anthropic's Claude demonstrates superior agentic reasoning, tool use and complex coding capabilities. Gemini consistently outperforms others in multimodal understanding—analyzing images, videos, or complex visual data. Grok Heavy delivers impressive capabilities for large-scale, complex reasoning tasks. And even the Kimi+Groq combination provides unbeatable speed and cost-effectiveness with solid quality. and so on...

Each model represents thousands of engineering hours and billions of parameters optimized for specific strengths. OpenAI's training emphasizes creativity and research depth. Anthropic focused heavily on safety and reasoning capabilities. Google's Gemini was built from the ground up for multimodal understanding. Each represents different philosophical approaches to intelligence.

Here's what I've learned from building production systems: trying to force one model to handle everything is like asking a Formula 1 car to also be the best van, suv, and family car. It's technically possible to build something that does all four, but it won't excel at any of them.

The magic happens when you combine their unique strengths. In our internal mixture-of-agent systems, we route different types of queries to different models based on their strengths. A user asking for creative writing gets OpenAI. Complex reasoning problems go to Claude. Visual analysis routes to Gemini. This isn't just about performance—it's about delivering experiences that feel genuinely superintelligent because they're leveraging the best of each system.

When I demonstrate this approach to other founders and engineers, the response is always the same: "Holy shit, this feels like actual AGI." That's because it is—AGI isn't one perfect model, it's the intelligent orchestration of multiple specialized capabilities.

The Right Toolset Matters

Let me share an analogy that crystallized this insight for me. Imagine trying to solve a complex problem on a computer with only basic tools—a text editor, calculator, and web browser—versus having a fully equipped workstation with IDEs, databases, analytics tools, design software, and specialized applications for every task. The difference in productivity and output quality is dramatic.

Yet in AI, many in the industry have convinced themselves that a single model with a few general tools is sufficient, simply because AI can browse, code, and theoretically write tools on the fly. This approach is fundamentally backwards.

The reality is that the professional world operates on an incredibly diverse ecosystem of specialized tools. A surgeon's operating room contains hundreds of specialized instruments, each optimized for specific procedures. A film studio has dedicated equipment for lighting, sound, cameras, editing, and post-production. A financial trading floor runs on real-time data feeds, risk management systems, algorithmic trading platforms, and compliance monitoring tools. The depth and diversity of these toolsets is what enables professional-quality results—something that a few general-purpose tools simply cannot achieve.

Consider how the smartest professionals actually work: they invest heavily in building and curating the best pre-built tools for their domain. When a new task arrives, they immediately reach for the optimal tool to solve it efficiently. While they maintain the capability to create solutions from scratch when necessary, they will invest in building new tools when emerging needs become significant enough. This approach maximizes efficiency, quality, and flexibility—it's the proven formula for professional excellence.

The AI industry will begin to recognize this pattern. The most successful AI applications aren't trying to do everything with basic tools—they're building comprehensive toolsets that match the complexity and specialization of their target domains. The companies that understand this principle will deliver better AI experiences, but not general AI experiences.

Multi-Agent is the Future

The "one super agent does it all" idea sounds appealing in theory, but it fundamentally misunderstands how professional excellence actually works. You wouldn't hire an MIT PhD to simultaneously serve as your chef, driver, developer, and designer. Even if they possessed the raw capability to perform all these roles reasonably well, it would be neither cost-effective nor optimal for results.

The economics are straightforward: specialization drives both efficiency and quality. Consider the MIT PhD making burgers—yes, they could probably create a decent burger, but at what cost? You're paying PhD-level compensation for work that a skilled line cook could do better, faster, and at a fraction of the price. Meanwhile, you're wasting their specialized expertise on tasks that don't require it.

This isn't just about cost—it's about unlocking exponential capability through intelligent specialization. The most successful organizations don't hire generalists to do everything; they assemble teams of specialists who excel in complementary areas. A world-class restaurant succeeds not because one person can do everything, but because the head chef, sous chef, pastry chef, and sommelier each bring deep expertise to their domain.

The real breakthrough isn't in individual agent capability—it's in the orchestration layer that enables these agents to communicate, hand off work, iterate on each other's contributions, and collectively deliver results that no single agent could achieve alone. Like a championship sports team, the magic happens not in any individual player, but in how they work together—anticipating each other's moves, covering weaknesses, amplifying strengths, and creating possibilities that exist only through collaboration.

The Path Forward

Let's acknowledge the elephant in the room: Some argue that model companies will eventually close their APIs, keeping their best capabilities for internal use only, which would make multi-model orchestration impossible. This concern is understandable, but it misses a crucial economic reality.

The market always rewards whoever delivers the best user experience, regardless of the company business needs. The fundamental question: Which approach actually delivers superior experiences to users? You have your own answer.

Here's the reality about the AI landscape: it's vast and diverse enough to support multiple approaches. In any thriving ecosystem, there will always be those who choose to build closed systems and those who choose to remain open. This coexistence isn't a bug—it's a feature. Just as the broader technology industry has both Apple's closed ecosystem and Google's open ecosystem, both serving different user needs and thriving simultaneously, the AI world will naturally evolve toward a similar balance. Some users will prefer the seamless integration of closed systems, while others will prefer the flexibility and capability of multi-model approaches.

What matters is that this diversity of approaches creates the competitive pressure that drives innovation forward. Companies pursuing vertical integration will push the boundaries of what single models can achieve. Companies pursuing orchestration will push the boundaries of what collaborative intelligence can accomplish. Both approaches will produce breakthroughs that benefit users.

Multi-model, multi-agent systems acknowledge this reality and turn it into a competitive advantage. By intelligently combining the best capabilities from different sources—whether they're open APIs, partnerships, or internal models—these systems can deliver experiences that consistently exceed what any single model could provide. The user gets the best creative writing capabilities, the best reasoning capabilities, the best visual understanding, and the best coding assistance—all in one seamless experience.

My bet is on the approach that starts with user needs and works backward to the optimal technical solution, rather than starting with internal capabilities and hoping users will accept the limitations. In a world where both open and closed systems will coexist, the winners will be those who can intelligently navigate this landscape to deliver the best possible user experiences, regardless of whether that requires one model or many.