SLMs vs LLMs: 10% of the cost, 100% of the accuracy?

Large language models have dominated the AI conversation — but are small language models (SLMs) actually the future?In this episode of TechFirst, host John Koetsier sits down with Andy Markus, SVP & Chief Data and AI Officer at AT&T, to unpack how small language models are delivering enterprise-grade accuracy at a fraction of the cost and latency of massive LLMs.Andy explains how AT&T uses SLMs for:• Contract analysis at massive scale• Network analytics and outage root-cause analysis • Fraud detection and enterprise knowledge systems• AI-driven “field coding” and agent-based workflowsThey also dive into the rise of agentic AI, how structured “archetypes” replace risky vibe coding, and why the future of software development may be humans supervising autonomous AI systems rather than writing every line of code.If you’re building AI for real-world, high-scale use cases — especially in enterprise environments — this conversation is essential.⸻GuestAndy MarkusSVP & Chief Data and AI Officer, AT&TFormer SVP at Time Warner Media⸻👉 Subscribe for more deep dives on AI, technology, and the future of innovation:https://techfirst.substack.com⸻00:00 – Why the future of AI might be small00:55 – What is a small language model (SLM)?01:45 – From LLM hype to enterprise reality02:25 – Solving accuracy, cost, and latency at once03:05 – How small is “small”? Parameters explained03:55 – Where SLMs work best inside enterprises04:45 – Contract analysis and enterprise vector stores05:35 – Network analytics and outage root-cause analysis06:45 – AI as a super-charged network engineer07:35 – Choosing high-ROI AI use cases08:20 – 4× ROI: measuring real business impact09:00 – AI field coding vs risky vibe coding10:10 – Archetypes, super agents, and structured AI workflows11:15 – What software engineers still need to do12:10 – From punch cards to natural language programming13:10 – Human-in-the-loop vs autonomous AI agents14:10 – How small can models really get?15:10 – Responsible AI at enterprise scale16:00 – The future of agentic AI and autonomy17:10 – Why AI output is finally becoming predictable18:10 – Final thoughts on where AI is headed