In this episode, we explore what happens when AI leaves the lab and enters real-world production. We examine why most AI projects fail at deployment, how production systems differ fundamentally from research models, and what it takes to operate large language models reliably at scale.

The discussion focuses on the engineering, organizational, and governance challenges of deploying probabilistic systems, along with the emerging architectures that turn LLMs into agents capable of planning, tool use, and autonomous action.

This episode covers:

• Why most AI projects fail in production
• Research vs. production AI: reliability, consistency, and scale
• Build vs. buy trade-offs for LLMs
• Hidden costs: prompt drift, prompt engineering, and inference
• Evaluation, monitoring, and governance in real systems
• Agent architectures and AI as infrastructure

This episode is part of the Adapticx AI Podcast. Listen via the link provided or search “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.

Sources and Further Reading

Additional references and extended material are available at:

https://adapticx.co.uk

Season 7 begins at a turning point. AI is no longer confined to research papers and demos—it is deployed, operational, and shaping real-world systems at scale. This season focuses on what changes when models move from experiments to production infrastructure.

We explore how organizations build, monitor, and maintain AI systems whose behavior is probabilistic rather than deterministic. What reliability means when models can adapt, fail in unexpected ways, and influence high-stakes decisions. And how engineering practices evolve when AI is treated not as a tool, but as a collaborator embedded in workflows.

The season also looks ahead to the next frontier: reasoning models, planning systems, and autonomous agents capable of using tools, coordinating tasks, and acting toward goals. Alongside these capabilities come urgent questions of safety, governance, and control—how risks are identified, how responsibility is enforced, and how oversight scales with capability.

Finally, we examine one of the defining debates of this era: open versus closed models. Who should control powerful AI systems, how transparency affects innovation and safety, and what these choices mean for the long-term trajectory toward AGI.

Season 7 is about AI in the world—how it behaves in production, how it is governed, and how today’s decisions shape what comes next.

This episode is part of the Adapticx AI Podcast. Listen via the link provided or search “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.

Sources and Further Reading

Additional references and extended material are available at:

https://adapticx.co.uk

In this episode, we explore how large language models evolved from passive text generators into agentic systems that can use tools, take actions, collaborate, and operate inside dynamic environments. We explain the shift from “knowing” to “doing,” and why this transition marks one of the most significant changes since the Transformer.

We break down what defines agentic AI, how agents plan and act through tool use, and why multi-agent systems outperform single models on complex, real-world tasks. The episode also covers the emerging agent frameworks, real business impact, and the safety and governance challenges that come with autonomy.

This episode covers:

• The gap between text generation and real-world action
• What defines agentic AI: autonomy, reactivity, proactivity, learning
• Tool use as the bridge from reasoning to execution
• Agent lifecycles: planning, action, observation, refinement
• Single-agent limits and multi-agent systems (MAS)
• Popular agent frameworks (LangChain, LangGraph, AutoGen, CrewAI)
• Enterprise, science, and productivity impacts
• Safety, latency, memory, and responsibility challenges

This episode is part of the Adapticx AI Podcast. Listen via the link provided or search “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.

Sources and Further Reading

Additional references and extended material are available at:

https://adapticx.co.uk

In this episode, we explore how open-source large language models transformed AI by breaking proprietary barriers and making advanced systems accessible to a global community. We examine why the open movement emerged, how open LLMs are built in practice, and why transparency and reproducibility matter.

We trace the journey from large-scale pre-training to instruction tuning, alignment, and real-world deployment, showing how open models now power education, tutoring, and specialized applications—often matching or surpassing much larger closed systems.

This episode covers:

• Why open LLMs emerged and what they changed
• Model weights, transparency, and reproducibility
• Pre-training, instruction tuning, and alignment
• Open LLMs in education and specialized domains
• RAG, multi-agent systems, and trust
• Small specialized models vs large proprietary models

This episode is part of the Adapticx AI Podcast. Listen via the link provided or search “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.

Sources and Further Reading

Additional references and extended material are available at:

https://adapticx.co.uk

In this episode, we explore how AI crossed a critical threshold—from powerful but expert-only systems to tools anyone can use naturally. We trace the usability revolution that turned large language models into conversational, intuitive interfaces, and explain why this shift mattered as much as raw intelligence.

We walk through the technical breakthroughs behind this change—from static word embeddings and LSTMs to Transformers, scale, and RLHF—and connect them to human-centered design principles like effectiveness, efficiency, and satisfaction. The episode also examines how usability is measured, why ChatGPT succeeded despite imperfections, and how multimodal and efficient architectures are shaping the next phase of AI interaction.

This episode covers:

• Why early AI systems were hard to use
• Static vs contextual language understanding
• Transformers, scale, and zero-/few-shot learning
• RLHF and conversational alignment
• Usability metrics (SUS) and adoption drivers
• Multimodal models and efficiency-focused designs
• AI as a universal natural-language interface

This episode is part of the Adapticx AI Podcast. Listen via the link provided or search “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.

Sources and Further Reading

Additional references and extended material are available at:

https://adapticx.co.uk

In this episode, we explore how large language models learned to follow instructions—and why this shift turned raw text generators into reliable AI assistants. We trace the move from early, unaligned models to instruction-tuned systems shaped by human feedback.

We explain supervised fine-tuning, reward models, and reinforcement learning from human feedback (RLHF), showing how human preference became the key signal for usefulness, safety, and control. The episode also looks at the limits of RLHF and how newer, automated alignment methods aim to scale instruction learning more efficiently.

This episode covers:

• Why early LLMs struggled with instructions
• Supervised instruction tuning (SFT)
• RLHF and reward modeling
• Helpfulness, truthfulness, and safety trade-offs
• Bias, cost, and scalability of alignment
• The future of automated alignment

This episode is part of the Adapticx AI Podcast. Listen via the link provided or search “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.

Sources and Further Reading

Additional references and extended material are available at:

https://adapticx.co.uk

In this episode, we examine why GPT-3 became a historic turning point in AI—not because of a new algorithm, but because of scale. We explore how a single model trained on internet-scale data began performing tasks it was never explicitly trained for, and why this forced researchers to rethink what “reasoning” in machines really means.

We unpack the scale hypothesis, the shift away from fine-tuning toward task-agnostic models, and how GPT-3’s size unlocked zero-shot and few-shot learning. This episode also looks beyond the hype, examining the limits of statistical reasoning, failures in arithmetic and logic, and the serious risks around hallucination, bias, and misinformation.

This episode covers:

• Why GPT-3 marked the shift from specialist models to general-purpose systems
• The scale hypothesis: how size alone unlocked new capabilities
• Zero-shot, one-shot, and few-shot learning explained
• In-context learning vs fine-tuning
• Emergent abilities in language, translation, and style
• Why GPT-3 “reasons” without symbolic logic
• Failure modes: arithmetic, logic, hallucination
• Bias, fairness, and the risks of training on the open internet
• How GPT-3 reshaped prompting, UX, and AI interaction

This episode is part of Season 6: LLM Evolution to the Present of the Adapticx AI Podcast.

This episode is part of the Adapticx AI Podcast. Listen via the link provided or search “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.

Sources and Further Reading

Additional references and extended material are available at:

https://adapticx.co.uk

Season 6 explores how large language models evolved from research systems into everyday AI tools. We focus on the breakthroughs that unlocked reasoning, instruction-following, usability, and agentic behavior—and why this era marks a true turning point in AI.

Episodes this season:

• GPT-3 & Zero-Shot Reasoning — How scale unlocked emergent capabilities
• Instruction Tuning & RLHF — Aligning models with human intent
• ChatGPT, Gemini & Usability — Why interface design changed everything
• The Open-Source LLM Movement — How open models reshaped innovation
• Agents, Tools & Ecosystems — From models to collaborative systems

This season traces the moment AI moved from the lab into daily life.

This episode is part of the Adapticx AI Podcast. Listen via the link provided or search “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.

Sources and Further Reading

Additional references and extended material are available at:

https://adapticx.co.uk