The Audit Podcast – IA on AI: Dealing with Unmanageable Expectations of AI within Your Company

Date: October 8, 2025
Host: Trent Russell
Guest Panel (via replay from The AI Fundamentalists):

• Andrew Clark (AI Expert, Ex-Capital One Principal Machine Learning Auditor, CTO & Co-Founder at Monitar)
• Sid Mangalik (AI Fundamentalists Co-Host)
• Additional AI Fundamentalists Co-Host

Episode Overview

This episode tackles the growing—and often unrealistic—expectations of artificial intelligence (AI) within organizations, specifically as they relate to internal audit. Through a replay of The AI Fundamentalists, Trent Russell sets the stage for a foundational discussion of the current state of AI, recent advancements (and lack thereof), and emerging limitations. The panel focuses on the release of GPT-5, the plateauing of generative AI progress, synthetic data shortcomings, and practical enterprise use cases—helping listeners calibrate expectations and prepare clear-eyed responses to AI hype in their own companies.

Key Discussion Points & Insights

1. The Slowdown in AI Progress: “Moore’s Law” No More

• AI’s Progress Slowing: The panel questions why GPT-5 received so much fanfare despite offering little practical advancement over previous versions.
  • “It doesn't feel like it's much bigger, doesn't feel like it's significantly better. It doesn't feel like this is changing the game the way that, like, 2 to 3 to 4 did.” – Sid Mangalik [04:20]
• Moore’s Law Fading for AI: The rate of improvement in large language models is dropping, just as transistor growth in hardware has plateaued.
  • “The 'Moore’s Law of AI' is really slowing down. ... The magnitudinal improvements are definitely not there anymore.” – AI Fundamentalists Co-Host [04:42]
• Market and Fundraising Pressure: OpenAI’s release is speculated to be driven by business pressures rather than technical breakthroughs.
  • “I think OpenAI was probably pushed to do some of it because of fundraising and things...” – AI Fundamentalists Co-Host [04:42]

2. Critical Reception of GPT-5

• Incremental, Not Transformative: The upgrade to GPT-5 appears minor; many users experience disruptions rather than improvements.
  • “When this version came out, they found their stuff was broken. It's almost like they lost an employee.” – Andrew Clark [07:38]
• Unfinished Features & Branding Over Substance: GPT-5 lacks features found in its predecessors, fueling confusion.
  • “They've tried to ... map between the old models and the new models, but the new models aren't done yet. They don't even have image out, they don't have audio in, they don't have audio out.” – Sid Mangalik [06:15]

3. Enterprise vs. Consumer AI Direction

• Different Approaches: OpenAI is seen as drifting in its target market, while Anthropic focuses on business use cases, offering stronger enterprise controls and guardrails.
  • “It seems they're kind of rudderless and they don't know who their market is anymore. ... It's very expensive and it's not targeted.” – AI Fundamentalists Co-Host [09:47]
• Business Utility Still Unproven: Despite targeting business, reliability and clarity for critical use cases like health remain huge concerns.
  • “I still would be very, very wary of using it for those [enterprise] needs.” – Sid Mangalik [09:16]

4. Synthetic Data: The Quality Dilemma

• Running Out of Training Data: LLMs have already ingested most available human-written text; relying on synthetic data hasn’t created better models.
  • “We've kind of given it every single piece of written human language that we have ... what's left? Use synthetic data. And we're seeing that synthetic data is not giving great pre-training for models that are already enormous.” – Sid Mangalik [12:49]
• Model Decay: Training on AI-generated (synthetic) text accelerates model quality decline.
  • “Synthetic data is synthetic. ... you're not going to write, you know, Homer’s Iliad by a GPT ... It's not going to be that same level of creativity.” – AI Fundamentalists Co-Host [15:29]

5. Prompt Engineering and Homogenization of Language

• Herding Toward Uniformity: As users develop similar prompting habits, the linguistic diversity that once enriched models is disappearing, contributing to model stagnation.
  • “We have all kind of started talking to these models in the same way that we all kind of started talking to Google ... Losing that diversity and that range ... is absolutely hurting it.” – Sid Mangalik [17:15]

6. Risks and Misuses in Enterprise Contexts

• Health and Therapy Use Is Alarming: A notable share of LLM interaction is in healthcare and therapy—far beyond their intended or safe application.
  • “That last one scares me ... State of Illinois finally put out laws saying, like, this ... is not a therapist. Like, you cannot use the AI as a therapist.” – Andrew Clark [11:46]
• Security Guardrails Improving, But Work Remains: GPT-5 reportedly enhances output verification, but evasion is still possible.

7. The Future: Is This the LLM High-Water Mark?

• Limited Incremental Gains on Horizon: Future improvements in LLMs will be modest, focused on polish—not leaps in intelligence or reasoning.
  • “The capabilities you're seeing now are probably the max that we're going to see from LLM-based systems.” – Sid Mangalik [20:27]
• Need for AI Paradigm Shift: Real progress may come from other AI methods such as reinforcement learning, not bigger LLMs.
  • “This LLM craze is going to start ... this could be the high-water mark ... I think we might be starting to reach the top of that hype.” – AI Fundamentalists Co-Host [18:22]
  • “I almost think it's going to be a renaissance of some of the previous things. Like let's go revisit reinforcement learning with the new computing power...” – AI Fundamentalists Co-Host [21:55]

Notable Quotes & Memorable Moments

• On Overpromising & Hype:
  “I had a CAE yesterday tell me that someone told them, outside consultants came in and said, you can entirely replace internal audit with AI. And she went, how? And they went, I don't know, I read it in a blog somewhere.” — Trent Russell / Host [00:01]

• On GPT-5’s Release:
  “To be honest, I haven't actually played with GPT5. ... The less emotive, the less creative. I don't know what kind of guardrails and things they might have put on it for that.” — AI Fundamentalists Co-Host [08:43]

• On the Capacity Ceiling:
  “I don't want to say that AI is done for, that we're not going to get better. I think we absolutely will. But ... if GPT-5 ... feels like it was just barely better than 4, this is probably the point where we're seeing the end of the use of this method as a means of advancing.” — Sid Mangalik [20:27]

• On AI in Health and Therapy:
  “It would drive doctors crazy that they were diagnosing themselves on the Internet ... it's scary. ... Illinois finally put out laws saying ... you cannot use the AI as a therapist.” — Andrew Clark [11:46]

• On AI Hype Cycles:
  “AI is a broad field and we've had several AI winters ... it used to be complete expert systems, then ... reinforcement learning, then ... deep learning, then ... LLMs. We go through these cycles ... it's going to be a renaissance of some of the previous things.” — AI Fundamentalists Co-Host [21:55]

Key Timestamps

• 00:01 – Introduction by Trent Russell: Addressing unrealistic expectations for AI in audit and background on guest panel.
• 04:20 – Discussion begins on GPT-5’s lack of revolutionary change.
• 06:15 – Complaints of GPT-5’s unfinished, regression in features.
• 07:38 – Disruption as GPT-5 “breaks” user processes.
• 09:16 – Enterprise push for AI models and associated skepticism.
• 11:46 – Growing (and risky) use of LLMs in healthcare and therapy.
• 12:49 – The problem of feeding models with synthetic, AI-generated data.
• 15:29 – Why synthetic data cannot replicate original human creativity.
• 17:15 – Prompt engineering and the homogenization of user queries.
• 18:22 – High-water mark of the LLM craze; calling for other AI approaches.
• 20:27 – Realistic expectations for future LLM improvement.
• 21:55 – Comparing competing models and discussing likely future paradigms.

Conclusion: Practical Takeaways for Audit & Enterprise

• Set Realistic AI Expectations: Don’t overpromise to management; LLM gains are modest, and limitations are significant.
• Guard Against Hype: Major advances will likely not come from bigger LLMs, but from alternative AI methods.
• Synthetic Data is Not a Panacea: Quality and creativity depend on real human inputs; synthetic data fails to measure up.
• Enterprise Use Caution: LLMs in critical sectors like healthcare can be risky—do not treat them as a panacea, and beware of regulatory limitations.
• Keep Watching the Field: The next leap will likely require rethinking foundational AI approaches, not just scaling up existing models.

For internal auditors and enterprise leaders: This episode provides a direct, unvarnished look at where generative AI is truly at in 2025, equipping listeners to navigate hype cycles, respond thoughtfully to executive demands, and plan for a more grounded AI future.