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Over the past few years, AI headlines have been dominated by scale.
Bigger models. More parameters. Higher benchmark scores.
But as I talk with founders, engineers, and business leaders, one theme keeps coming up: size alone isn’t solving real problems.
A 175B parameter model is impressive on paper, but when a hospital needs reliable AI for triage, or a security analyst needs to decode a malware script, “close enough” just doesn’t cut it.
That’s why the next frontier of AI is not bigger general-purpose models — it’s domain-specific LLMs.
Recently, Google released research into two of the most important vertical models so far:
SecLM → a security-focused LLM built for cybersecurity workflows.
MedLM (built on Med-PaLM) → a healthcare-focused model designed for clinical reasoning and medical Q&A.
And the lesson from both?
👉 The future belongs to AI that understands your industry’s language, data, and workflows.
Let’s break it down.
🔐 Cybersecurity: Why SecLM Is a Game-Changer
Cybersecurity is one of the toughest environments for AI to thrive in.
Here’s why:
Constantly evolving threats → New malware, phishing campaigns, and exploits appear daily.
Operational toil → Security teams drown in alerts, triaging hundreds of false positives manually.
Talent shortage → There aren’t enough trained analysts to meet demand.
Most general LLMs fail here. They don’t see enough security data in training. They don’t handle sensitive use cases (like malware analysis). And they aren’t designed for real-time reasoning over security logs.
That’s where SecLM steps in.
🔎 What SecLM Does Differently
SecLM isn’t just a chatbot. It’s a multi-layered security API that blends:
LLM reasoning → to translate natural-language queries into security queries.
Retrieval-Augmented Generation (RAG) → to pull in fresh threat intelligence.
Tool use + planning frameworks → to automate investigation, clustering, and remediation.
Concrete use cases:
Decode and analyze an obfuscated PowerShell script.
Summarize the latest threat intel into a one-page report for the CISO.
Identify attack paths inside your infrastructure.
Suggest remediation steps tailored to your environment.
An example response from the SecLM platform using a base64 decoding tool and the SecLM model to analyze an obfuscated PowerShell command used in a ‘living off the land’ attack
💡 Why it matters: SecLM is designed for accuracy, context, and security-specific tasks. It saves analysts hours and elevates them from “alert triagers” to “strategic defenders.”
🩺 Healthcare: MedLM and Responsible AI Innovation
Healthcare presents a different challenge.
Doctors face an ever-growing mountain of medical knowledge. Every year, thousands of new studies and papers are published. On top of that, patient interactions require nuanced reasoning and responsible, safe recommendations.
Generic LLMs can answer trivia about symptoms — but would you trust them to guide treatment?
That’s why Google built MedLM, based on Med-PaLM, a model aligned specifically for medicine.
Example of clinician review of Med-PaLM 2
📊 MedLM’s Breakthroughs
Expert-level accuracy → Scored 86.5% on USMLE-style questions (well beyond the passing threshold).
Patient support → Helps triage patient messages, flagging urgent ones for clinicians.
Clinical reasoning → Provides on-demand consults for unfamiliar diseases.
Workflow optimization → Improves intake by adapting questions based on patient responses.
Feedback loops → Analyzes clinician-patient conversations and provides coaching feedback.
Med-PaLM 2 reached expert-level performance on the MedQA medical exam benchmark
🧪 What Makes MedLM Different
Evaluation beyond accuracy → MedLM is scored on factuality, helpfulness, health equity, and potential harm.
Multi-step validation → From retrospective data → observational trials → interventional clinical studies.
Trust-first design → Built with clinicians, not just for them.
💡 Why it matters: MedLM is more than just “AI that knows medicine.” It’s a framework for how to responsibly deploy AI in any regulated, high-stakes domain.
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🌍 The Bigger Shift: From Horizontal to Vertical AI
SecLM and MedLM are just the start.
The bigger story is this: we’re moving from horizontal LLMs → to vertical, industry-specific AI.
General-purpose models (like GPT-4 or Claude) will still power broad use cases. But for industries where accuracy, compliance, and context are life-or-death, specialized models will dominate.
Where else will this play out?
Finance → fraud detection, compliance copilots, risk modeling.
Legal → contract review, case law copilots, regulatory research.
Manufacturing → predictive maintenance, IoT-powered safety copilots.
Customer Service → industry-tuned copilots with domain-specific knowledge.
Think of it this way:
👉 The future question won’t be “Which model is biggest?” but “Which model best understands my industry’s workflows?”
💡 Lessons for Builders, Startups & Business Leaders
For Builders: The next wave of startup opportunities will come from vertical AI. The horizontal copilots space is saturated. But domain-specific copilots are still wide open.
For Enterprises: The ROI will come from specialized copilots trained on your sector’s data, not generic AI copilots.
For Leaders: Partnerships with experts (clinicians, security analysts, compliance officers) are critical. Domain expertise + AI = moat.
For Everyone: Responsible AI isn’t optional. Accuracy, fairness, and validation frameworks must be built in from day one.
📝 Key Takeaways
General-purpose LLMs are powerful, but insufficient in high-stakes domains.
Cybersecurity (SecLM) and healthcare (MedLM) show us the future: domain-specific AI.
The magic formula is LLMs + RAG + tools + domain expertise.
Builders should look for vertical AI opportunities — that’s where the whitespace is.
The next AI advantage won’t come from size. It’ll come from depth.
💡 My advice if you’re a builder or leader:
Stop asking “what can GPT do?”
Start asking: “What would an AI trained on my industry’s language, data, and workflows unlock?”
That’s where the real breakthroughs, and businesses will come from.