Podcast Summary: The Artificial Intelligence Show – Ep. 163: AI Answers - AI Environmental Concerns, Agentic Workflows, SEO Impact, The Future of Creative Careers, & Human-First Processes

Date: August 21, 2025
Hosts: Paul Roetzer (Founder and CEO, Marketing AI Institute & SmartRx)
Co-host: Kathy McPhillips (Chief Marketing Officer, Marketing AI Institute & SmartRx)
Note: Mike Kaput was not present; Kathy co-hosted this AI Answers Q&A episode.

Overview

This episode is part of the “AI Answers” special Q&A series, where Paul and Kathy answer real, unscripted questions gathered from their popular “Intro to AI” and “Scaling AI” educational sessions. These questions reflect concerns and emerging priorities from business leaders and practitioners navigating the rapidly evolving AI landscape.

Main themes:

Environmental concerns of AI
AI’s cultural and linguistic biases
Legal uncertainties in AI-generated content
Practical AI adoption and workflow integration
The impact of AI on SEO, search engines, and creative careers
Human-first processes, governance, and ethical considerations

Key Discussion Points and Insights

1. Urgent Environmental Concerns in AI (05:23)

Immediate issue: Data centers require immense power, and not all of it will be clean energy.
"Oracle is planning to spend a billion dollars to power an OpenAI data center with gas turbines. Like that's not great for the environment." [05:31] – Paul
Industry “bet”: Short-term environmental damage is being justified by AI labs and government with the hope that advanced AI will help solve larger, long-term climate problems.
Practical tip: Use smaller, more efficient models when possible – akin to “turning off the lights when you leave the room” (07:27).

2. Language, Culture, and Bias in AI Models (08:09)

Most large models are US-based and English-centric; bias is present at every stage.
Non-English-speakers often use AI in English for better results.
Market forces may demand more local language support: “OpenAI said that I think their largest user base is actually out of India…they’re going to have to adapt the products.” [09:50] – Paul

3. Risks, Ownership, and Legal Gray Areas in AI-Generated Media (10:25)

Current status: AI-generated outputs (images, text, video) cannot be copyrighted in the US.
Complication: Human involvement (proof, audit trail) is critical if you seek copyright protection.
“You want to have the human as deeply in the loop as possible ... make sure you go through that process.” [13:34] – Paul
Brands must stay alert to deepfakes and understand contracts with outside creators must specify AI usage restrictions.

4. Experimenting with and Building AI Agents (15:40)

Definition: Agents are AI systems that take actions to achieve goals—levels of autonomy vary.
Most current agents still require substantial human input.
Where to start: Use Gemini or ChatGPT for deep research tasks or try Agent AI by Dharmesh Shah (HubSpot).
"That's an agent at work. So the human set the project gave the goal. The agent develops its plan ... then it comes back and creates the output." [17:00] – Paul
Resource: Their deep research webinar demonstrates this hands-on.

5. Single Ecosystem vs. Cross-Checking with Multiple Platforms (18:33)

Paul often builds and tests with both Gemini and ChatGPT for major projects, then selects whichever performs better but continues using both for critical tasks or cross-checking.
"Sometimes they come out with roughly the same output, verifies it, sometimes you get a little different thing." [19:37]

6. Choosing Built-in vs. Standalone AI Tools (22:23)

Most companies will follow legacy productivity software patterns—standardizing on Google, Microsoft, or potentially OpenAI.
Challenge: Built-in models (e.g., Gemini in Docs) may be less powerful than standalone tools.
Employees often encounter frustration with restricted/“watered down” internal versions.

7. Website Guidance for AI Systems and SEO Shifts (24:43)

Tools (like Cloudflare) now allow websites to block LLMs, but best practice is unclear.
Major shift: As Google and AI-powered search answers more questions directly, website traffic is dropping.
"We just can't rely on search engine traffic the way we used to." [26:29] – Paul

8. Future of Search Engines and the Shift to Voice (29:27)

Search is undergoing radical change; AI assistants answering directly will displace traditional web navigation.
The next interface may be voice (“if Siri actually becomes intelligent” [30:37]).
Uncertainty prevails: "Whatever we think a search engine is today looks nothing like that if voice becomes a dominant interface." [31:45]

9. Selecting Tools: Transparency, Governance, and Environmental Impact (32:46)

Critical policy reminder: Generative AI usage must be covered by clear company policy.
New risks (e.g., “computer use” agents that control/screens) require explicit prohibition in company policies.
Collaboration between legal, IT, and business users is vital.

10. AI’s Role in Community Building and Content Moderation (35:09)

Automation should relieve staff of low-value, repetitive tasks, freeing them for real human interaction.
“Automate the things that are low impact, low human...to free your people up, to spend more time on the human connection side.” [35:09] – Paul
Human engagement remains irreplaceable; automation without dehumanization is their principle ("automation without dehumanization" [37:29]).

11. Best Practices for AI-Driven Workflow Automation (38:52)

Start with: Defining and mapping workflows, then assign where AI can safely add value.
Always break tasks down, identify what should remain human-led, and secure cross-department review for sensitive or risky steps.

12. Mistakes in AI Adoption – When Not to Automate (41:00)

Not every task needs AI; sometimes the answer is “more human, not more AI.”
Simple rule-based automation can be preferable; don't force-fit AI where it doesn't add value.

13. Adoption: Small Business vs. Enterprise (42:30)

Small businesses act nimbly, with minimal procurement process; enterprise adoption is slower, with risk reviews and bureaucracy.
In large companies, sometimes business units “just go” if central IT lags.

14. Will AI Fluency Be Required for Executives? (45:20)

Absolutely: “AI literacy is maybe the most important skill moving forward at all levels.” [45:20] – Paul
Leadership legitimacy and decision-making will depend on AI understanding; employees will lose patience if executives can’t keep up.

15. How Can Creatives Thrive in an Age of AI? (47:08)

AI will supercharge great creatives ("superpowers"), democratize content for all, but quality and originality will still win.
"The people who are already good to great are just going to 10x up..." [48:45]
Resist the fear of replacement; use AI to automate the less-fulfilling parts of creative work instead.

16. Coding/Technical Careers for the Next Generation (52:42)

Technical skills (like coding/game design) teach problem-solving and focus—even if precise tasks change, these are universally valuable.
Question the ROI of expensive CS degrees as AI capabilities expand.
"The technical skills, the behaviors, the traits developed are valuable." [54:38]

17. Fact-Checking AI: The Verification Gap (55:51)

AI hallucinations/errors are diminished but not eliminated.
Never trust outputs blindly, especially for critical work. Human review and domain expertise are essential.
"Is this the best you can do?" – The internal standard for all deliverables, whether AI-assisted or not. [57:18]

18. Most Important Ethical Principle Today (58:48)

Human-centeredness: AI should unlock human potential, not replace it.
Backstory: SmartRx’s logo (a black hole) was inspired by the hope that AI can “slow time down,” making life and work more fulfilling by giving time back to people.

19. Data Privacy, Compliance, and AI Regulation (61:00)

Stay compliant with both existing and evolving laws/regulations for data, independent of AI.
Legal and risk/governance teams must be deeply involved in all decisions, as the regulatory environment remains dynamic.

20. Breakthrough and Overhyped AI Applications (62:04)

Overhyped for now: AI agents—true autonomy isn’t here yet (but will matter in the near future).
Underrated: Reasoning models—most business leaders haven’t realized their potential for strategic, nuanced knowledge work.
"Once you do [a deep research project], you can't look at anything the same…" [63:18]

Notable Quotes and Moments

"It's great to assess workflows. It's great to look at problems differently. But AI isn't always the answer. Sometimes more human is the answer." [41:04] – Paul
"Automation without dehumanization." [37:29] – Paul
"I think that's the problem we see now is like people who don't understand that these things get stuff wrong all the time ... the dominoes start falling. We're like, this looks amazing ... but it's all based on flawed assumptions and data." [56:37] – Paul
"If you just took a spreadsheet ... of the 25 things I do in my job, which ones do I get fulfillment from ... Take the things where you say no, and those are the first things you should automate." [51:54] – Paul

Timestamps for Key Segments

[05:23] – Environmental concerns for AI/data centers
[08:09] – Cultural/linguistic bias in models
[10:25] – Legal gray area for AI-generated content
[15:40] – Getting started with AI agents
[18:33] – Using multiple platforms vs. standardizing
[22:23] – Choosing built-in vs. standalone apps
[24:43] – SEO, content strategy, and LLMs
[29:27] – Search engines & coming voice revolution
[32:46] – Choosing tools: transparency, governance, environment
[35:09] – Automation in building communities
[38:52] – Integrating AI in workflow automation
[41:00] – Pitfalls of force-fitting AI
[42:30] – Adoption: SMB vs. enterprise
[45:20] – Leadership and AI fluency
[47:08] – Creative careers in an AI world
[52:42] – Coding/tech skills for Gen Alpha/Gen Z
[55:51] – Fact checking and the verification gap
[58:48] – The most important ethical principle
[61:00] – Data privacy and regulation
[62:04] – Overhyped and underappreciated AI apps

Final Thoughts

Throughout, Paul and Kathy emphasize real-time learning, responsible adoption, and keeping a human focus amid rapid transformation. They urge listeners to stay curious, iterative, and always aware of both the capacities and the boundaries of today’s AI tools.

For further learning:

Visit Academy SmarterX AI for on-demand courses and resources.
Watch the deep research and AI Academy webinars referenced in the episode.

Next steps: Continue building your AI literacy, and above all—don’t automate away the human value that makes your organization thrive.

Podcast Summary: The Artificial Intelligence Show – Ep. 163: AI Answers - AI Environmental Concerns, Agentic Workflows, SEO Impact, The Future of Creative Careers, & Human-First Processes

Overview

Main themes:

Environmental concerns of AI
AI’s cultural and linguistic biases
Legal uncertainties in AI-generated content
Practical AI adoption and workflow integration
The impact of AI on SEO, search engines, and creative careers
Human-first processes, governance, and ethical considerations

Key Discussion Points and Insights

1. Urgent Environmental Concerns in AI (05:23)

Immediate issue: Data centers require immense power, and not all of it will be clean energy.
"Oracle is planning to spend a billion dollars to power an OpenAI data center with gas turbines. Like that's not great for the environment." [05:31] – Paul
Industry “bet”: Short-term environmental damage is being justified by AI labs and government with the hope that advanced AI will help solve larger, long-term climate problems.
Practical tip: Use smaller, more efficient models when possible – akin to “turning off the lights when you leave the room” (07:27).

2. Language, Culture, and Bias in AI Models (08:09)

Most large models are US-based and English-centric; bias is present at every stage.
Non-English-speakers often use AI in English for better results.
Market forces may demand more local language support: “OpenAI said that I think their largest user base is actually out of India…they’re going to have to adapt the products.” [09:50] – Paul

3. Risks, Ownership, and Legal Gray Areas in AI-Generated Media (10:25)

Current status: AI-generated outputs (images, text, video) cannot be copyrighted in the US.
Complication: Human involvement (proof, audit trail) is critical if you seek copyright protection.
“You want to have the human as deeply in the loop as possible ... make sure you go through that process.” [13:34] – Paul
Brands must stay alert to deepfakes and understand contracts with outside creators must specify AI usage restrictions.

4. Experimenting with and Building AI Agents (15:40)

Definition: Agents are AI systems that take actions to achieve goals—levels of autonomy vary.
Most current agents still require substantial human input.
Where to start: Use Gemini or ChatGPT for deep research tasks or try Agent AI by Dharmesh Shah (HubSpot).
"That's an agent at work. So the human set the project gave the goal. The agent develops its plan ... then it comes back and creates the output." [17:00] – Paul
Resource: Their deep research webinar demonstrates this hands-on.

5. Single Ecosystem vs. Cross-Checking with Multiple Platforms (18:33)

Paul often builds and tests with both Gemini and ChatGPT for major projects, then selects whichever performs better but continues using both for critical tasks or cross-checking.
"Sometimes they come out with roughly the same output, verifies it, sometimes you get a little different thing." [19:37]

6. Choosing Built-in vs. Standalone AI Tools (22:23)

Most companies will follow legacy productivity software patterns—standardizing on Google, Microsoft, or potentially OpenAI.
Challenge: Built-in models (e.g., Gemini in Docs) may be less powerful than standalone tools.
Employees often encounter frustration with restricted/“watered down” internal versions.

7. Website Guidance for AI Systems and SEO Shifts (24:43)

Tools (like Cloudflare) now allow websites to block LLMs, but best practice is unclear.
Major shift: As Google and AI-powered search answers more questions directly, website traffic is dropping.
"We just can't rely on search engine traffic the way we used to." [26:29] – Paul

8. Future of Search Engines and the Shift to Voice (29:27)

Search is undergoing radical change; AI assistants answering directly will displace traditional web navigation.
The next interface may be voice (“if Siri actually becomes intelligent” [30:37]).
Uncertainty prevails: "Whatever we think a search engine is today looks nothing like that if voice becomes a dominant interface." [31:45]

9. Selecting Tools: Transparency, Governance, and Environmental Impact (32:46)

Critical policy reminder: Generative AI usage must be covered by clear company policy.
New risks (e.g., “computer use” agents that control/screens) require explicit prohibition in company policies.
Collaboration between legal, IT, and business users is vital.

10. AI’s Role in Community Building and Content Moderation (35:09)

Automation should relieve staff of low-value, repetitive tasks, freeing them for real human interaction.
“Automate the things that are low impact, low human...to free your people up, to spend more time on the human connection side.” [35:09] – Paul
Human engagement remains irreplaceable; automation without dehumanization is their principle ("automation without dehumanization" [37:29]).

11. Best Practices for AI-Driven Workflow Automation (38:52)

Start with: Defining and mapping workflows, then assign where AI can safely add value.
Always break tasks down, identify what should remain human-led, and secure cross-department review for sensitive or risky steps.

12. Mistakes in AI Adoption – When Not to Automate (41:00)

Not every task needs AI; sometimes the answer is “more human, not more AI.”
Simple rule-based automation can be preferable; don't force-fit AI where it doesn't add value.

13. Adoption: Small Business vs. Enterprise (42:30)

Small businesses act nimbly, with minimal procurement process; enterprise adoption is slower, with risk reviews and bureaucracy.
In large companies, sometimes business units “just go” if central IT lags.

14. Will AI Fluency Be Required for Executives? (45:20)

Absolutely: “AI literacy is maybe the most important skill moving forward at all levels.” [45:20] – Paul
Leadership legitimacy and decision-making will depend on AI understanding; employees will lose patience if executives can’t keep up.

15. How Can Creatives Thrive in an Age of AI? (47:08)

AI will supercharge great creatives ("superpowers"), democratize content for all, but quality and originality will still win.
"The people who are already good to great are just going to 10x up..." [48:45]
Resist the fear of replacement; use AI to automate the less-fulfilling parts of creative work instead.

16. Coding/Technical Careers for the Next Generation (52:42)

Technical skills (like coding/game design) teach problem-solving and focus—even if precise tasks change, these are universally valuable.
Question the ROI of expensive CS degrees as AI capabilities expand.
"The technical skills, the behaviors, the traits developed are valuable." [54:38]

17. Fact-Checking AI: The Verification Gap (55:51)

AI hallucinations/errors are diminished but not eliminated.
Never trust outputs blindly, especially for critical work. Human review and domain expertise are essential.
"Is this the best you can do?" – The internal standard for all deliverables, whether AI-assisted or not. [57:18]

18. Most Important Ethical Principle Today (58:48)

Human-centeredness: AI should unlock human potential, not replace it.
Backstory: SmartRx’s logo (a black hole) was inspired by the hope that AI can “slow time down,” making life and work more fulfilling by giving time back to people.

19. Data Privacy, Compliance, and AI Regulation (61:00)

Stay compliant with both existing and evolving laws/regulations for data, independent of AI.
Legal and risk/governance teams must be deeply involved in all decisions, as the regulatory environment remains dynamic.

20. Breakthrough and Overhyped AI Applications (62:04)

Overhyped for now: AI agents—true autonomy isn’t here yet (but will matter in the near future).
Underrated: Reasoning models—most business leaders haven’t realized their potential for strategic, nuanced knowledge work.
"Once you do [a deep research project], you can't look at anything the same…" [63:18]

Notable Quotes and Moments

"It's great to assess workflows. It's great to look at problems differently. But AI isn't always the answer. Sometimes more human is the answer." [41:04] – Paul
"Automation without dehumanization." [37:29] – Paul
"I think that's the problem we see now is like people who don't understand that these things get stuff wrong all the time ... the dominoes start falling. We're like, this looks amazing ... but it's all based on flawed assumptions and data." [56:37] – Paul
"If you just took a spreadsheet ... of the 25 things I do in my job, which ones do I get fulfillment from ... Take the things where you say no, and those are the first things you should automate." [51:54] – Paul

Timestamps for Key Segments

[05:23] – Environmental concerns for AI/data centers
[08:09] – Cultural/linguistic bias in models
[10:25] – Legal gray area for AI-generated content
[15:40] – Getting started with AI agents
[18:33] – Using multiple platforms vs. standardizing
[22:23] – Choosing built-in vs. standalone apps
[24:43] – SEO, content strategy, and LLMs
[29:27] – Search engines & coming voice revolution
[32:46] – Choosing tools: transparency, governance, environment
[35:09] – Automation in building communities
[38:52] – Integrating AI in workflow automation
[41:00] – Pitfalls of force-fitting AI
[42:30] – Adoption: SMB vs. enterprise
[45:20] – Leadership and AI fluency
[47:08] – Creative careers in an AI world
[52:42] – Coding/tech skills for Gen Alpha/Gen Z
[55:51] – Fact checking and the verification gap
[58:48] – The most important ethical principle
[61:00] – Data privacy and regulation
[62:04] – Overhyped and underappreciated AI apps

Final Thoughts

For further learning:

Visit Academy SmarterX AI for on-demand courses and resources.
Watch the deep research and AI Academy webinars referenced in the episode.

Next steps: Continue building your AI literacy, and above all—don’t automate away the human value that makes your organization thrive.

#163: AI Answers - AI Environmental Concerns, Agentic Workflows, SEO Impact, The Future of Creative Careers, & Human-First Processes

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Summary

Podcast Summary: The Artificial Intelligence Show – Ep. 163: AI Answers - AI Environmental Concerns, Agentic Workflows, SEO Impact, The Future of Creative Careers, & Human-First Processes

Overview

Key Discussion Points and Insights

1. Urgent Environmental Concerns in AI (05:23)

2. Language, Culture, and Bias in AI Models (08:09)

3. Risks, Ownership, and Legal Gray Areas in AI-Generated Media (10:25)

4. Experimenting with and Building AI Agents (15:40)

5. Single Ecosystem vs. Cross-Checking with Multiple Platforms (18:33)

6. Choosing Built-in vs. Standalone AI Tools (22:23)

7. Website Guidance for AI Systems and SEO Shifts (24:43)

8. Future of Search Engines and the Shift to Voice (29:27)

9. Selecting Tools: Transparency, Governance, and Environmental Impact (32:46)

10. AI’s Role in Community Building and Content Moderation (35:09)

11. Best Practices for AI-Driven Workflow Automation (38:52)

12. Mistakes in AI Adoption – When Not to Automate (41:00)

13. Adoption: Small Business vs. Enterprise (42:30)

14. Will AI Fluency Be Required for Executives? (45:20)

15. How Can Creatives Thrive in an Age of AI? (47:08)

16. Coding/Technical Careers for the Next Generation (52:42)

17. Fact-Checking AI: The Verification Gap (55:51)

18. Most Important Ethical Principle Today (58:48)

19. Data Privacy, Compliance, and AI Regulation (61:00)

20. Breakthrough and Overhyped AI Applications (62:04)

Notable Quotes and Moments

Timestamps for Key Segments

Final Thoughts

Summary

Podcast Summary: The Artificial Intelligence Show – Ep. 163: AI Answers - AI Environmental Concerns, Agentic Workflows, SEO Impact, The Future of Creative Careers, & Human-First Processes

Overview

Key Discussion Points and Insights

1. Urgent Environmental Concerns in AI (05:23)

2. Language, Culture, and Bias in AI Models (08:09)

3. Risks, Ownership, and Legal Gray Areas in AI-Generated Media (10:25)

4. Experimenting with and Building AI Agents (15:40)

5. Single Ecosystem vs. Cross-Checking with Multiple Platforms (18:33)

6. Choosing Built-in vs. Standalone AI Tools (22:23)

7. Website Guidance for AI Systems and SEO Shifts (24:43)

8. Future of Search Engines and the Shift to Voice (29:27)

9. Selecting Tools: Transparency, Governance, and Environmental Impact (32:46)

10. AI’s Role in Community Building and Content Moderation (35:09)

11. Best Practices for AI-Driven Workflow Automation (38:52)

12. Mistakes in AI Adoption – When Not to Automate (41:00)

13. Adoption: Small Business vs. Enterprise (42:30)

14. Will AI Fluency Be Required for Executives? (45:20)

15. How Can Creatives Thrive in an Age of AI? (47:08)

16. Coding/Technical Careers for the Next Generation (52:42)

17. Fact-Checking AI: The Verification Gap (55:51)

18. Most Important Ethical Principle Today (58:48)

19. Data Privacy, Compliance, and AI Regulation (61:00)

20. Breakthrough and Overhyped AI Applications (62:04)

Notable Quotes and Moments

Timestamps for Key Segments

Final Thoughts