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Practical AI

2025 was the year of agents, what's coming in 2026?

51 min episode · 2 min read

Episode

51 min

Read time

2 min

Topics

Investing, Sales & Revenue, Artificial Intelligence

AI-Generated Summary

Key Takeaways

  • Agent Implementation Success: Effective AI agents require domain expertise to configure prompts, select data sources, and integrate tools like MCP servers. Organizations lacking this expertise face high failure rates, with Gartner predicting 40% of projects will fail by 2027 despite 11% having agents in production.
  • Reasoning Model Trade-offs: Models like Claude Opus 4.5 and OpenAI o1 generate intermediate reasoning tokens before final outputs, enabling senior-level coding capabilities. However, each reasoning token requires separate model inference runs, dramatically increasing latency and computational costs for production applications.
  • Power Infrastructure Bottleneck: GPU availability no longer limits AI advancement; power consumption does. Speculators purchase decommissioned power plants anticipating reactivation needs. Energy requirements now drive geopolitical policy decisions, with AI infrastructure investments facing community resistance over power demands and environmental impact.
  • AI Engineering Skill Set: The emerging valuable role combines data science, software development, and system architecture to build MCP servers, connect databases, integrate RAG systems, and orchestrate multiple AI services. This integration expertise remains complex enough to resist automation for years.

What It Covers

Hosts Daniel Whitnack and Chris Benson review 2025 as the year AI agents emerged, examining successful implementations, reasoning model advances, infrastructure challenges, and predictions for 2026's increasingly complex AI ecosystem.

Key Questions Answered

  • Agent Implementation Success: Effective AI agents require domain expertise to configure prompts, select data sources, and integrate tools like MCP servers. Organizations lacking this expertise face high failure rates, with Gartner predicting 40% of projects will fail by 2027 despite 11% having agents in production.
  • Reasoning Model Trade-offs: Models like Claude Opus 4.5 and OpenAI o1 generate intermediate reasoning tokens before final outputs, enabling senior-level coding capabilities. However, each reasoning token requires separate model inference runs, dramatically increasing latency and computational costs for production applications.
  • Power Infrastructure Bottleneck: GPU availability no longer limits AI advancement; power consumption does. Speculators purchase decommissioned power plants anticipating reactivation needs. Energy requirements now drive geopolitical policy decisions, with AI infrastructure investments facing community resistance over power demands and environmental impact.
  • AI Engineering Skill Set: The emerging valuable role combines data science, software development, and system architecture to build MCP servers, connect databases, integrate RAG systems, and orchestrate multiple AI services. This integration expertise remains complex enough to resist automation for years.

Notable Moment

One host describes spending weeks researching an autonomy project, then crafting a detailed prompt that generated six weeks worth of production-quality code in six minutes, representing a transformative workflow shift enabled by late 2025 model capabilities.

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Episode Transcript

Welcome to the Practical AI podcast, where we break down the real world applications of artificial intelligence and how it's shaping the way we live, work, and create. Our goal is to help make AI technology practical, productive, and accessible to everyone. Whether you're a developer, business leader, or just curious about the tech behind the buzz, you're in the right place. Be sure to connect with us on LinkedIn, x, or Blue Sky to stay up to date with episode drops, behind the scenes content, and AI insights. You can learn more at practicalai.fm. Now onto the show. Welcome to a new year of Practical AI and an episode with just Chris and I where, we try to keep you fully connected with everything that's happening in the AI world, which is a lot these days, both last year and this year. But, I'm Daniel Whitnack. I I am CEO at Prediction Guard, and I'm joined as always by my cohost, Chris Benson, who is a principal AI research engineer at Lockheed Martin. Happy New Year, Chris. Hey. Happy New Year, Daniel. It's, 2026, probably the fastest moving AI year ever coming up here. First well, every every new year, I guess, has been the fastest AI movie. Well, I guess since we started the podcast, you know, whatever, eight years ago It was a safe thing for me to say. There you go. Yeah. Yeah. Yeah. Safe thing for you to say. I mean, granted, these last few years have been a little bit frantic, in relation to the years prior to that with the podcast, which felt, you know, in retrospect, seem a little bit chill. Yeah. But Yeah. It, it definitely seems like 2025 was a big year. 2026 will be a big year. And so as we're coming into the new year for our listeners, usually, we try to do some type of, we don't have a strict format here because we're pretty casual, but some type of discussion of things that happened in 2026, you know, themes looking for or things that happened in 2025. I'm already a year ahead, I guess. Things that happened in 2025 and things that may or may not happen in 2026. Usually, our predictions are wrong as are all predictions. I'm okay with that. All models are wrong, but hopefully this hopefully this podcast will be useful. Yeah. So, interesting interesting times, Chris. Interesting dynamics in our world in all sorts of ways. But if we if we hone in on AI, I think at the at the beginning of last year, if I'm remembering correctly, there were a couple things we talked about. One of those things or or certainly at least an, theme that we've talked about a lot this year, which if we were to categorize the year 2025, I don't know if you would agree, Chris, but it does seem like the year that we transitioned to talk about AI agents. It was sort …

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Books, tools, and gear mentioned in this episode

SignalCast may earn commission on purchases via these links.

Tools

  • by Framer

    Sponsors: Framer (https://framer.com/practicalai)
  • by Anthropic

    Models like Claude Opus 4.5 and OpenAI o1 generate intermediate reasoning tokens before final outputs, enabling senior-level coding capabilities.
  • Effective AI agents require domain expertise to configure prompts, select data sources, and integrate tools like MCP servers.
  • by OpenAI

    Models like Claude Opus 4.5 and OpenAI o1 generate intermediate reasoning tokens before final outputs, enabling senior-level coding capabilities.
  • by Prediction Guard

    Sponsors: Prediction Guard (https://predictionguard.com)

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