Domain-Specific Models and Superintelligence Networks
How domain-specific models in the fields of Medicine, Academia, Law and Finance differ from the LLM Interfaces used by the public in training data, alignment protocols, user interface and hallucinogenic reliability; problematizing the network theory of Superintelligence based on Data Privacy, Rights and Alignment.
The world has quickly embraced ChatGPT. We have let Open AI’s sleek white chat screen become synonymous with AI because of how applicable and easy to use it is. We engage with it in our studies, our work and our daily personal lives. Its flexibility, adaptability and efficiency has cemented its role as the face of artificial intelligence. But while the majority of us are chatting with this LLM, fascinating developments in AI are happening far away from our chat logs. These developments are called domain-specific AI systems.
In laboratories, hospitals, law firms, and financial institutions, domain-specific AI systems are reshaping professional workflows far from public view. These are not general-purpose models trained to do everything decently well. Instead, they are purpose-built systems trained to do one thing incredibly well. Whether it’s diagnosing a rare disease or drafting a tricky legal brief, these AI systems are starting to outperform not just other models, but humans too.
General-purpose models like ChatGPT are trained on massive datasets pulled from across the internet. They are designed to be extensive, adaptable, and conversational in their interactions. But their very breadth can actually become a weakness in domains that require deep knowledge, transparency, and accountability. In the fields of scientific research, finance, law and medicine, hallucinations are unacceptable, faulty or disorganized data is a hazard and issues with misalignment cannot be tolerated. These are fields where stakes are high and domain-specific models are necessary.
Google has created MedGemma, an open-source model trained specifically for healthcare. The model reaches nearly 88% accuracy on MedQA, a test designed to assess the medical knowledge and clinical reasoning ability of AI systems based on United States Medical Licensing Examination (USMLE) questions. Not only does it perform better than generalists, but it does so more efficiently and at a lower computational cost. In China, Tsinghua University’s Institute for AI Industry Research created an entire Agent Hospital. This hospital simulates a working healthcare system, with 42 physician agents covering 21 specialties. These AI doctors have already successfully processed over 10,000 simulated patient cases, demonstrating both speed and accuracy. Most impressive is how the agents work in coordination, following the same workflows that human teams do in live Hospitals like triaging, consulting, and following up. These domain-specific AI aren’t just tools. They’re fully functioning systems.
There are equally impressive breakthroughs in academic research and scientific research in particular. FutureHouse is a nonprofit lab based in San Francisco. It is building multi-agent platforms to accelerate scientific discovery in chemistry and biology. Their agents don’t just process data; they reason through scientific hypotheses, simulate experiments, and build on each other’s findings. One of their models, ether0, specializes in complex chemical inference. Another, Robin, coordinates teams of agent researchers. They’re not designed to be conversational, flexible or supportive, they’re designed to make new scientific discoveries and they’re doing just that.
The legal and financial worlds are also following suit. While ChatGPT might be able to explain your parking ticket or draft a letter to your landlord about your rights surrounding a leaky faucet, law firms are operating on completely different systems for support. They are using specialized models like Harvey and CoCounsel to analyze case law, draft motions, and prepare for depositions. These models are trained not on the vast scores of data on Reddit and Wikipedia, but specifically on court transcripts, legal textbooks, and precedent. In finance, BloombergGPT is a domain specific model that can digest decades of proprietary market data and interpret and predict economic trends with an accuracy that no public or general model could hope to match. These fields in which the slightest issues with training data, alignment or results would prove catastrophic cannot and will not rely on larger, less focused systems.
You don’t see these domain specific systems in your newsfeed or as beta tests to try out because they aren’t built for public consumption. They live behind passcodes and paywalls within the locked offices of hospitals, hedge funds, research institutes, and government labs. They aren’t optimized for friendliness, charm or usability, they’re optimized for results and they are kept private even when they deliver. This, of course, raises deeper questions. Many of these systems are closed-source and kept private to protect sensitive data or proprietary knowledge. However, some of the systems we have analysed like MedGemma are open source and they offer extraordinary public value to those who can use them. There is a growing tension, however, because open domain-specific models are increasingly scraped and absorbed into larger general-purpose models, which learn from their outputs without attribution, transparency or consent. The specialized, careful labor that builds these systems can be cannibalized to serve general AI systems that are broader but not necessarily better. So what is the point in keeping something specific if it’s going to be reabsorbed into the general anyway? The answer lies in purpose and integrity. Domain-specific models offer transparency, customizability, and local deployment. They can be audited, verified, and trusted in high-stakes environments. They’re often more efficient, more interpretable, and, most importantly, they are more accountable. The value of a domain model isn’t only in its broadness of data, but in its quality of data and its alignment: its understanding of what matters in a specific context, and how to do that one thing right. With the richness and depth of data training these domain specific models, the absence of poor quality or contradictory data as well as the ability to give strict, field-based alignment direction, the support of these systems is far more valuable to the high stakes environments and trained professionals operating them than the responses from a broader model with more universal alignment protocols.
Beyond questions of credit and scraping, especially in these fields, another issue arises: privacy. Domain-specific models, especially when they are run locally or within a secured institution, offer a level of data protection that general-purpose AI can’t match. A hospital using MedGemma on its own infrastructure never sends patient data to a third party and is able to better control and protect patient’s rights. A research team using a local chemistry model doesn’t have to wonder who’s watching their hypotheses and technical progress as they move through their research process. The model is theirs, and as such, so is the data. General LLMs, even subscription based models, almost always require you to send your data to external servers. Whether or not it is used for training, a company may have access to it. This may not pose an issue for smaller conversations and questions but for legal, medical and financial advice, this can have a huge impact. With domain-specific systems, privacy is often built in by design, primarily because the industries that use them demand it.
These domain-specific models have proven their power to the researchers looking to achieve AGI or artificial general intelligence, which is what the broader LLMs like ChatGPT, Gemini and Claude are chasing. There is now a growing movement among AI researchers that domain-specific models may actually be the pathway to AGI and even onward to superintelligence (ASI). Instead of a single monolithic brain, the future may lie in a federated network of expert systems—scientific, medical, legal, financial—each deeply trained, each plugged into a larger cognitive architecture.
But this raises a worrisome issue for those critics following along. These domain-specific models are so powerful because they are often built in very unique, controlled environments. Rather than the data scraping, dubious consent tactics and open use of the general LLMs, these purpose built systems are, from conception, governed by privacy, trained on restricted data, and overseen by institutional ethics boards. If they are looked to to be the backbone of future general AI systems, we must ask: how will the principles that shaped their design like data locality, interpretation, razor sharp alignment protocols, and safety concerns be able to scale to a broader audience? What happens when expert systems come together to form something more general? It could be less private, less accountable, less accurate and in its UX-UI evolution, become a completely different entity. Perhaps the best outcome is not one or the other—but both. General-purpose models might serve as flexible scaffolding which lead you to the right place, while domain-specific agents keyed into more detailed data supply the depth. This would leave us with AI not as a monolith, but as a network of coordinated intelligences, each doing what it does best. If that's the case, AI will grow to inhabit a living architecture with a generalized reception desk asking all the right questions and pointing you towards the specialized model that would suit you best.
Works Consulted
AIR @ Tsinghua University. Agent Hospital: A Generalist Agent for Multidisciplinary Medical Diagnosis and Treatment Planning. arXiv:2405.02957. 2024. https://arxiv.org/abs/2405.02957
Bloomberg. BloombergGPT: A Large Language Model for Finance. 2023. https://www.bloomberg.com/company/press/bloomberggpt/
Dedhia, Bhishma, Yuval Kansal, and Niraj K. Jha. Bottom-up Domain-specific Superintelligence: A Reliable Knowledge Graph is What We Need. arXiv:2507.13966. 2025. https://arxiv.org/abs/2507.13966
FutureHouse. Humanity’s Last Exam: How Agents Struggle with Scientific Reasoning. July 2025. https://www.futurehouse.org
Google DeepMind. Teaching Machines the Language of Biology: Scaling Large Language Models for Single-Cell Analysis. Research Blog, 2024. https://research.google/blog
Google Research. MedGemma: Our Most Capable Open Models for Health AI Development. July 2025. https://research.google/blog/medgemma-our-most-capable-open-models-for-health-ai-development/
King's College London. MEDITRON: A Suite of Open Multilingual Medical Foundation Models. arXiv:2311.16079. 2023. https://arxiv.org/abs/2311.16079
Laneau, Anthony. “MedGemma Opens the Next Chapter in Health AI.” Medium. July 2025. https://medium.com/@anthonylaneau/medgemma-opens-the-next-chapter-in-health-ai-f6c3ccf5d84c
Meta. Superintelligence at Meta: Building Responsibly as We Move Toward the Future. July 2025. https://www.meta.com/superintelligence
Meta AI. “Mark Zuckerberg Says Superintelligence Is in Sight.” The Guardian, 30 July 2025. https://www.theguardian.com/technology/2025/jul/30/zuckerberg-superintelligence-meta-ai
Meta AI. “Mark Zuckerberg’s Vision for AI: Open Superintelligence That Empowers People.” Meta Newsroom, July 2025. https://www.meta.com/blog/technology/superintelligence-open-source-ai
Microsoft Research. BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining. 2022. https://github.com/microsoft/BioGPT
Ought. Elicit: The AI Research Assistant. 2024. https://elicit.org
Rysysth Technologies. “China’s Breakthrough AI Agent Hospital Could Transform Healthcare.” July 2025. https://rysysthtechnologies.com/insights/chinas-breakthrough-ai-agent-hospital
The MedQA Dataset. A Large-scale Medical Question Answering Dataset for USMLE-style Exams. OpenAccess AI Datasets, 2020. https://github.com/jind11/MedQA
The Verge. “Zuckerberg Says Meta Is Building Open-Source Superintelligence — Carefully.” July 30, 2025. https://www.theverge.com/ai-artificial-intelligence/715951/mark-zuckerberg-meta-ai-superintelligence