Podcast Summary: AI in 2026 – 7 Reasons Why the Pace of AI This Year Will Far Exceed 2025

Everyday AI Podcast – An AI and ChatGPT Podcast
Host: Jordan Wilson
Date: January 6, 2026

Episode Overview

This episode of the Everyday AI podcast, hosted by Jordan Wilson, dives into why 2026 is set to outpace 2025 in AI adoption and real-world impact. Jordan, leveraging his martech background, outlines the "five pillars of AI acceleration" for 2026 and explains the transformative changes that have made AI fundamentally more useful and accessible. The tone is lively, no-nonsense, and geared toward practical application for business leaders and everyday users.

Key Discussion Points and Insights

1. Reasoning by Default: Models as True Thought Partners

[03:04 – 11:10]

• AI models have shifted from being glorified text generators to acting as intelligent reasoning agents.
• In 2025, only about 7% of ChatGPT queries used reasoning models. Most didn’t realize the exponential leap between classic transformers and newer reasoning models.
• "The gap between how most people use AI today and what it's actually capable of is mountainous." – Jordan Wilson [00:17]
• Key improvements: Models now exhibit human-like planning and decision-making, enabling non-technical users to access expert-level outputs without complex prompting.
• "Models from yesteryear… weren't that good, if I'm being honest. Right?… You had to really almost be obsessive to get human level output out of large language models." – Jordan Wilson [10:10]

2. Agentic Scaffolding: AI That Acts, Not Just Responds

[11:11 – 18:05]

• Jordan demystifies "agentic scaffolding", where models autonomously use tools like code execution, web browsing, and memory.
• Modern AIs decide independently when to activate these abilities—moving from simple Q&A to holistic problem-solving.
• Example: AI planning a trip, not just finding flights but also checking calendars, integrating weather, booking, etc., often exceeding user expectations.
• "It is really thought partnership. I'm not using large language models very much anymore… for simple input output. It is really thought partnership." – Jordan Wilson [17:50]

3. Frictionless Data Integration: RAG Pipelines Become One-Click

[18:06 – 23:25]

• Retrieval-Augmented Generation (RAG) no longer requires complex backend work—connecting business data to AI models is now "one click."
• Major platforms (OpenAI, Anthropic, Google) now offer seamless integration of company data (e.g., Google Drive, Outlook) for grounded, relevant responses.
• "Having this ability to connect AI to business reality was a major bottleneck, but I don't think it is anymore." – Jordan Wilson [23:13]

4. Exponential Task Endurance: AI’s Human-Level Stamina

[23:26 – 30:50]

• AI task completion stamina (the "effective horizon") has skyrocketed. Models can now reliably accomplish projects that would take humans 5+ hours to complete, up from mere minutes in previous years.
• Example: Meter (METR) benchmarks show exponential jumps—doubling the complexity and duration of tasks AIs can handle every 4 months.
• “If the current trend continues… as early as 2027, maybe 2028, we'll be able to do a month of human work and get it correct.” – Jordan Wilson [27:23]
• Businesses often lag in awareness, with many still relying on outdated, free AI models. “Literally. Enterprise companies that are making billions of dollars are using in some instances the free version of ChatGPT, which is I would not recommend.” [29:52]

5. Economically Meaningful Work: Outperforming Human Experts

[30:51 – 35:13]

• The newest language models, especially GPT 5.2 Thinking Mode, produce real, economically valuable outputs—PowerPoints, spreadsheets, legal briefs, engineering documents, etc.—at expert level.
• Key benchmark: OpenAI's GDP Val evaluates how AIs perform across 1,300+ tasks from 44 occupations (graded blindly by experts). GPT 5.2 scored a win/tie rate of 74% versus experts, up from 38% for the previous model, and completed tasks 11x faster at less than 1% of the cost.
• “If you're still thinking of large language models as that cute cheeky chat GPT… No, large language models… instantly connect to your business data. They can think like experts, they can use tools that experts sometimes might not even know how to use. They can work for hours on end and they're doing work better than human experts that is more economically valuable.” – Jordan Wilson [34:07]

Notable Quotes & Memorable Moments

• "The gap between how most people use AI today and what it's actually capable of is mountainous." – Jordan Wilson [00:17]
• "Reasoning models… think, they plan, they can agentically start going down a certain path and then decide, oh, this isn't the right path. And they can then go backwards and start down a different path, much like a human would." – Jordan Wilson [10:45]
• "Models now know when to enable tool calling…without being told by the human." – Jordan Wilson [11:30]
• “Having this ability to connect AI to business reality was a major bottleneck, but I don't think it is anymore.” – Jordan Wilson [23:13]
• "AI models are going to be able to complete what would take a human a month." – Jordan Wilson [27:22]
• "It does better than human experts or at least a win tie rate of 3/4. It is 11 times faster and it costs less than 1%." – Jordan Wilson [33:35]
• "Sorry, consultants. It's gonna be a tough 2026..." – Jordan Wilson [32:05]

Timestamps for Important Segments

• [00:17] – Opening statement about the AI capability gap
• [03:04] – Introduction of the “five pillars of AI acceleration”
• [11:11] – Agentic scaffolding explained
• [18:06] – RAG made frictionless: AI/business data integration
• [23:26] – Exponential AI task endurance & the METR benchmarks
• [30:51] – Economically meaningful work, GDP VAL results

Additional Takeaways

• The episode stresses a necessary mindset shift: Businesses must view LLMs as operational partners rather than just productivity gimmicks.
• Proactive, personalized super assistants are becoming a reality, as highlighted by OpenAI’s Fiji Simo’s recent remarks.
• Real advice: Move your organization’s meaningful work inside powerful LLM interfaces and treat them as core productivity platforms, not just assistants.
• Jordan encourages listeners to upskill, join their AI Inner Circle community, and not get left behind as the acceleration curve becomes steeper.

Conclusion

2026 marks a new epoch for AI – with reasoning models as default, agentic capabilities, seamless business data integration, exponential task endurance, and proven economic impact, adoption is moving from fringe usage to business-critical workflows. The time to act is now.

Jordan’s energetic, practical guidance empowers everyone—technical or not—to leap ahead with AI this year.

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