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 workflows

They 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.

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Guest

Andy Markus

SVP & Chief Data and AI Officer, AT&T

Former SVP at Time Warner Media

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00:00 – Why the future of AI might be small

00:55 – What is a small language model (SLM)?

01:45 – From LLM hype to enterprise reality

02:25 – Solving accuracy, cost, and latency at once

03:05 – How small is “small”? Parameters explained

03:55 – Where SLMs work best inside enterprises

04:45 – Contract analysis and enterprise vector stores

05:35 – Network analytics and outage root-cause analysis

06:45 – AI as a super-charged network engineer

07:35 – Choosing high-ROI AI use cases

08:20 – 4× ROI: measuring real business impact

09:00 – AI field coding vs risky vibe coding

10:10 – Archetypes, super agents, and structured AI workflows

11:15 – What software engineers still need to do

12:10 – From punch cards to natural language programming

13:10 – Human-in-the-loop vs autonomous AI agents

14:10 – How small can models really get?

15:10 – Responsible AI at enterprise scale

16:00 – The future of agentic AI and autonomy

17:10 – Why AI output is finally becoming predictable

18:10 – Final thoughts on where AI is headed