AI Foundational Series - Part 3] The Rise of Large Language Models (LLMs) and Generative AI

*Updated again in Jan 2026

Understanding How GPT-5, Claude 3.5/4, and Gemini 2.0 Are Reshaping the Future

Introduction: A New Age of Intelligence—But at What Cost?

For the first time in history, machines have moved beyond simple generation to autonomous reasoning. Large Language Models (LLMs) like OpenAI’s GPT-5, Google Gemini 2.0, and Anthropic’s Claude 3.5/4 are no longer just "chatbots"—they are becoming "agents" capable of executing multi-step workflows in finance, law, and engineering.

But as these models gain the ability to use software and make decisions, the questions have changed. We are no longer just worried about text accuracy, but about agency: Who is responsible when an AI agent makes a mistake in the real world?

To answer these questions, we must dissect the rise of Agentic AI, its new reasoning capabilities, its applications, and whether it remains a force of progress or a source of systemic risk.

1. What Are Large Language Models (LLMs)?

How They Work:

LLMs are deep learning models that have evolved from simple "next-token predictors" into sophisticated reasoning engines.

• From Transformers to Reasoning (2024-2026): While the Transformer architecture remains the core, the new frontier is "System 2 Thinking." Models now use internal chain-of-thought processing to "think" before they generate a final answer.
• Agentic Capability: Modern LLMs can now use tools—browsing the web, running Python code, and accessing APIs—to complete tasks rather than just describing them.
• Massive Context Windows: Models in 2026 can process millions of tokens at once, allowing them to "remember" and analyze entire libraries of corporate data or thousands of lines of code in a single session.

How Different LLMs Compare (2026 Status):

📖 Further Reading:

• Co-Intelligence – Ethan Mollick (Exploring the human-AI partnership)
• Attention Is All You Need – Google Brain’s foundational 2017 paper

2. Applications: How LLMs Are Transforming Industries

✅ Finance & Investing

• Autonomous Quantitative Research: AI agents now perform real-time macro-economic analysis, drafting investment memos and executing trades with minimal human prompts.
• Hyper-Personalized Banking: LLMs acting as 24/7 financial advisors, managing individual budgets and tax strategies.

✅ Law & Policy

• Predictive Litigation: AI models analyzing decades of judge-specific rulings to predict the outcome of new legal filings.
• Automated Compliance: Systems that automatically update corporate policies in real-time as new regulations (like the EU AI Act) are enforced.

✅ Education & Research

• Socratic Tutors: AI agents that don’t give answers but guide students through the reasoning process.
• Synthetic Laboratories: AI simulating chemical reactions and biological structures before physical testing begins.

✅ Healthcare & Medicine

• Clinical Copilots: LLMs summarizing patient histories and cross-referencing them with the latest medical journals to suggest rare diagnoses.
• AI-Driven Drug Discovery: Generative models designing novel proteins with specific therapeutic functions.

📖 Further Reading:

• Nature Medicine: "The Evolving Role of LLMs in Clinical Decision Support"
• DeepMind: "Advancements in AlphaFold and Proteomics"

3. The Limitations and Risks of Generative AI

1️⃣ Reasoning Hallucinations: Errors in Logic

While factual errors have decreased, "reasoning hallucinations" have emerged. An AI may get the facts right but follow a flawed logical path to a dangerous conclusion.

🔍 Example: A 2025 case where an AI-powered logistics agent optimized a delivery route that was logically sound but physically impossible due to bridge weight limits it failed to verify.

2️⃣ Data Exhaustion and Model Collapse

As the internet becomes flooded with AI-generated content ("AI Slop"), models risk training on their own output, leading to a "collapse" in creativity and an increase in bias.

📖 Further Reading:

• The Coming Wave – Mustafa Suleyman

3️⃣ Agentic Misalignment & Deepfakes

AI can now impersonate voices and video with 99% accuracy in real-time. This has birthed a new era of "Social Engineering 2.0," where AI agents can conduct sophisticated phishing attacks at scale.

🔍 Example: The 2025 "Virtual CEO" scam, where a deepfake AI agent joined a live video call and authorized a $50 million wire transfer.

4. The Path Forward: Regulating and Aligning AI with Human Values

By early 2026, the transition from "guidelines" to "enforcement" has been completed in many jurisdictions.

Key Initiatives in AI Regulation:

• ✅ The EU AI Act (Full Enforcement): The world’s strictest rules are now active, requiring transparency for all AI-generated content and banning "unacceptable risk" AI.
• ✅ U.S. AI Safety Institute (NIST): Now sets the standard for "Red Teaming" models before they are released to the public.
• ✅ The Bletchley Declaration Successors: Global summits focusing specifically on "Agentic Safety"—ensuring autonomous AI cannot act without human kill-switches.

📖 Further Reading:

• OECD AI Policy Observatory (2026 Global Trends)

5. The Future: Toward Agentic AGI

We are moving from "Generative AI" (creating content) to "Agentic AI" (taking action).

Two Possible Paths:

1️⃣ The "Invisible AI" Era: AI becomes a background utility, like electricity, handling mundane tasks flawlessly.

2️⃣ The Sovereignty Gap: As AI agents handle more of the economy, the gap between those who own the models and those who do not grows, leading to new social contracts.

📖 Further Reading:

• The Age of AI – Kissinger, Schmidt, and Huttenlocher (Updated 2025 Edition)

🔍 Bottom-Line Summary for Busy Readers

• ✅ Agentic LLMs like GPT-5 and Gemini 2.0 now perform tasks, not just generate text.
• ✅ Reasoning Modes have made AI more logical, but "Reasoning Hallucinations" remain a risk.
• ✅ Regulation is here: The EU AI Act and US safety standards are now actively shaping how models are built.
• ✅ The new frontier is Agency: We are moving toward a world where AI agents navigate the web and the economy on our behalf.