The End of the Monolith: The Future of AI is an Ensemble of Models and Intelligent Orchestration
Models, Models, Models Everywhere, Self Evolving Algorithms and Agentic Systems with Orchestrators
Yes, some things are obvious when you stop to look back. The excitement (and perhaps hype) around Agentic AI Systems, the obvious progression towards World Models, and the “unending” VC money (and Policy support) going into energy systems (hey, this was always lagging, and if “AGI” (whatever the definition), is the trigger that pushes investments - yay, is all I have to say!)……VC’s (or Hedge Funds - hats off to the DeepSeek team who have seriously done so much to the field with their model releases (not to mention amazingly well written and shared papers!). (Qwen and the Kimi teams too!! Not to forget Meta’s Llama, AllenAI etc) chasing the best closed (and open) sourced/weight models - well, different paths can be taken towards achieving the same “ultimate goals”.
I want to examine one (or the many that feature similarly) - MAI-DxO and what Microsoft is doing.
The New Strategic Frontier in Artificial Intelligence
The evolution of foundation models in artificial intelligence continues at a breathtaking pace.
With each iteration, these systems (reasoning, agentic) become more capable, tackling increasingly complex tasks.
Yet, persistent challenges like hallucination, context decay, and the architectural limitations of transformers remain areas of active research.
Solutions involving enhanced memory structures, sophisticated data chunking, and entirely new algorithmic approaches are constantly being explored.
Progress is undeniable, but it has also revealed a crucial insight: the ultimate frontier may not lie in creating a single, perfect, monolithic model. Enter MAI-DxO (and many others highlighted below). My focus in this article is Microsoft. I have discussed Orchestration and other Agentic Systems in two prior articles.
While I was initially impressed by the staggering performance metrics of Microsoft's MAI-DxO—an 85.5% diagnostic accuracy in medicine—my focus has quickly shifted. The true breakthrough is not the result itself (as impressive as it is, truly!), but the methodology and strategy behind it.
In my opinion, MAI-DxO is a powerful signal of an industry-wide paradigm shift away from a "single model wins" scenario (as Satya Nadella has himself said many times, more obviously than others from various Labs, perhaps because of prevailing investment matters yet to be resolved), and towards a future defined by orchestration, ensembles, and collaborative agentic systems. Microsoft is not alone in this vision; across the globe, leading technology firms are converging on the same conclusion.
MAI-DxO: One Case Study in Orchestration
At its core, MAI-DxO (Medical AI Diagnostic Orchestrator) represents a new philosophy. Instead of relying on a single AI, it functions as a conductor, intelligently leveraging a diverse orchestra of specialized foundation models. It is model-agnostic, designed to integrate contributions from the OpenAI, Gemini, Claude, and Llama families, among others. Each model brings a unique strength to the ensemble: GPT-4's robust reasoning, Gemini's advanced multimodal capabilities, and Claude's safety alignment. The best-performing setup paired the orchestrator with OpenAI's o3 model, demonstrating a principle of collaboration, not replacement.
This approach is built on a solid mathematical foundation that has long been understood in machine learning.
The Bias-Variance Tradeoff: The core principle of ensemble methods is that by combining multiple, diverse models, their individual errors and biases tend to cancel each other out. The result is a collective output that is more robust and accurate than any single contributor.
Condorcet's Jury Theorem: This theorem mathematically proves that a larger group of independent, moderately accurate individuals will consistently outperform a small group of experts. By treating each AI model as a "juror," MAI-DxO builds a consensus that is statistically more likely to be correct.
What makes this more than a simple aggregation of outputs is the orchestration layer itself. It mirrors the dual-process theory of human cognition described by Daniel Kahneman as "System 1" and "System 2" thinking. The individual (reasoning/non-reasoning) AI models provide the rapid, intuitive "System 1" responses, while the orchestrator acts as the deliberate, analytical "System 2," verifying reasoning, running cost-benefit analyses, and guiding a sequential diagnostic process. I looked at this also with the recent Chess Championships.
This dynamic orchestration also implicitly leverages concepts like the Quality-Diversity Trade-off, where the system balances exploiting high-performance solutions with exploring diverse alternatives, and Bayesian Model Averaging, where the "weight" given to each model's opinion adapts over time based on its performance.
The Strategic Imperative: Beyond the Model Wars
Really, the development of MAI-DxO reveals a sophisticated corporate strategy from Microsoft. Rather than becoming wholly dependent on its multi-billion dollar investment in OpenAI, Microsoft is constructing an indispensable "meta-layer" that sits above the entire AI ecosystem. This positions them not as a mere user of foundation models, but as the essential platform that integrates and maximizes the value of all models.
It's a classic strategic move to avoid vendor lock-in and become the central hub in a distributed network, a vision clearly guided by the multi-agent systems expertise of Microsoft AI's leadership (or for that matter at Google Deepmind, Sakana AI Labs, Allen AI, Cohere etc).
So, this trend is not isolated to Redmond.
We are seeing a clear, industry-wide pivot towards agentic AI and orchestration platforms, marking (what feels like to me) a major inflection point in 2025. The kick in the butt to industry - really was the “January DeepSeek R1” moment (at it’s core, DeepSeek V3) - showing innovation surpassing constraints with ease. More could be done with less. Small Language Models could perform just as well with a little Fine-Tuning and one could table working models for < $500. And this naturally progressed towards “Agentic Systems as Orchestrators”! (Yes, Winner Takes All Model is not the path forward), And we mortals - enjoying the “Symphony of Models”.
So an appreciation also to the following:
Google DeepMind has long pioneered this approach. AlphaEvolve acts as an "algorithmic architect," orchestrating LLMs to discover superior sorting algorithms and achieve tangible resource savings in Google's own computing ecosystem. Their AI Co-Scientist utilizes a multi-agent system where a supervisor agent delegates tasks to specialized agents to generate and debate scientific hypotheses.
Sakana AI has developed core technologies for this paradigm. Their Adaptive Branching Monte Carlo Tree Search (AB-MCTS) is a powerful algorithmic engine for orchestrating multiple models at inference time, while their Darwin Gödel Machine (DGM) is a groundbreaking agentic system designed for recursive self-improvement.
Cohere, in a June 2025 partnership with Ensemble RCM, launched an agentic AI platform for healthcare revenue cycle orchestration, deploying a team of AI agents to manage complex financial workflows. Automating (don’t confuse these with Agentic systems) or Orchestrating Workflows - big money! Makes heaps of sense (Dollar and Cents too, if you’re counting).
Leading Chinese Labs are also heavily invested. In June 2025, Baidu launched a new search engine powered by a multi-agent system of four cooperating agents. Tencent's Hunyuan-A13B model features a dual-mode (fast and slow) reasoning capability, suggesting an internal orchestrator. This builds on a broader trend seen in open-source agentic frameworks like LangChain, Auto-GPT, and CrewAI.
These examples underscore a collective realization: the most complex and valuable problems require a team of specialists, not a single generalist.
Reading for the AI-Curious?
For those whose interest has been sparked by the concepts of AI orchestration and collaborative intelligence, I dusted these off my shelves recently, a quick reading list (if memory serves me right, and I may have left others off, but will update when i do remember - heck, I could be an AI model, with RAM issues (smirk):
"The Elements of Statistical Learning" by Hastie, Tibshirani, and Friedman - The mathematical foundation of ensemble methods.
"Ensemble Methods in Machine Learning" by Thomas Dietterich - A classic paper on why ensembles work.
"The Wisdom of Crowds" by James Surowiecki - The social science behind collective intelligence, directly applicable to orchestration.
"Thinking, Fast and Slow" by Daniel Kahneman - Essential for understanding the dual-system cognition that advanced AI systems now mimic.
"The Master Algorithm" by Pedro Domingos - Provides a broader context for the different paradigms of machine learning, including ensembles. The Symbolists, Connectionists, Evolutionaries, Bayesians, and Analogizers are the five machine learning tribes that Domingos looks at. Each tribe represents a different approach to AI and machine learning (much like the different model approaches taken by AI Labs). These tribes have created their own methodology and algorithms, but the search for a master algorithm tries to combine all of these techniques into a single, comprehensive answer. The question is can it? Or are we already seeing it done? Perhaps evolving - Metamorphosis - the pupa, becoming the butterfly
A New Era of Collaborative Intelligence & (I Hope) Equity
The implications of this shift are profound. In healthcare, it points to a future of "Collaborative Intelligence," where AI doesn't replace clinicians but augments their judgment with structured, transparent, and auditable reasoning.
The human-in-the-loop (HITL) approach is central to addressing the critical issue of accountability.
Furthermore, the ability of these systems to optimize processes and reduce costs promises to democratize expertise. When a rural clinic can access the collective diagnostic power of the world's leading medical knowledge via an AI orchestrator, it represents a fundamental step toward global healthcare equity.
The Road Ahead: From Monoliths to Intelligent Teams
MAI-DxO is more than a diagnostic tool; it is a proof of concept for a new way of building and deploying AI. The pattern is eminently generalizable to fields like legal research, financial analysis, and scientific discovery. The era of the monolithic AI system, with all its inherent limitations, is giving way to a more dynamic, robust, and ultimately more intelligent paradigm. We are no longer just building better AI models; we are learning to build better AI teams. The age of The Intelligent Orchestra has arrived……enjoy the Music, my friends…
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Super insightful thanks Suhnylla
Great insights. However, in my assessment, Microsoft’s new AI system for doctors ain’t good at all. Worse, they cherry-picked the results. Shame! https://sergeiai.substack.com/p/why-hippocratic-ai-and-nabla-can