Jeetu Patel knows a few AI secrets. 

As the President of one of the largest companies in the world, he's helped pave the AI adoption roadmap. 

At Cisco, they provide full-stack, enterprise AI solutions spanning infrastructure, security, observability, and operations to the world's largest companies. 

So naturally, Jeetu could write a legit playbook on what's slowing enterprises down in the AI fast lane and how they can overcome those bottlenecks. 

And naturally, Jeetu is gonna share it all with us. 

The 3 Big Obstacles Holding AI Adoption Back -- An Everyday AI Chat with Cisco President Jeetu Patel

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Topics Covered in This Episode:

1. Enterprise AI Adoption Rates & Challenges
2. AI Workflow Automation Phase Explained
3. Three Big Obstacles to AI Adoption
4. Infrastructure Constraints for Enterprise AI
5. Trust Deficit in AI Systems
6. Data Gaps Impacting AI Success
7. Measuring ROI on Enterprise AI Deployment
8. Future Trends: Agentic AI and Original Insights

Timestamps:

00:00 AI Adoption Challenges in Enterprise

05:18 AI Adaptation: The Key Strength

08:56 AI Infrastructure and Trust Challenges

10:23 Building Trust and Harnessing Data

13:27 Unsatiated Demand Signals Growth

19:12 Proactive AI Model Safeguards

22:07 AI Strategy and Business Growth

26:09 Key Metrics for AI Success

28:10 Guardrails for AI Vulnerabilities

31:34 AI Unlocking Revolutionary Discoveries

Keywords:

AI adoption, obstacles to AI adoption, enterprise AI, generative AI, AI strategies, chatbots, autonomous agents, workflow automation, business productivity automation, infrastructure for AI, AI power consumption, data center capacity, compute capacity, GPUs, Nvidia, AMD, network bandwidth, CapEx in AI, AI bubble, national security and AI, economic growth and AI, AI trust deficit, securing AI, AI safety, AI hallucinations, large language models, model unpredictability, AI guardrails, algorithmic jailbreak, AI security stack, AI defense, company data as moat, AI data pipeline, data gap in AI, machine data, human data, synthetic data, time series data, data correlation, AI model training, AI ROI, trust in AI systems, agentic workflows, future of AI, robotics, humanoid AI, physical AI, original insights with AI, economic prosperity with AI, AI-generated knowledge, workflow automation with AI agents, scaling AI in enterprises

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Did AI end up being a political force this year? 🤔

Did AI kill start to kill off influencers? 

Did we achieve AGI in 2025? 

We take our AI predictions seriously. And in January of this year, we made some kinda hot-take predictions about the above and more. 

How did we do? 

Tune in for Part 2 of our 2025 AI Roadmap Rewind as we give a State of AI and see if we were your AI BFF or if we led you astray. 

2025 AI Roadmap Rewind Human vs Machine, AI Models Shrink, and AGI No One Noticed -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

1. Anthropic $1.5B Copyright Lawsuit Settlement
2. AI Influencers Replace Human UGC Content
3. Non-Technical Vibe Coding Software Trend
4. Enterprise Reasoner Wrappers in AI Adoption
5. Rise of Virtual Machines for AI Agents
6. Political Impact of AI on US Policy
7. Global AI Regulations and EU AI Act
8. Narrow AI Agents Drive Business Value
9. LLM Memory and Context Advances in 2025
10. Shift: Large Language Models to Small Models
11. Mixture of Models Approach for Enterprises
12. AGI Benchmarks Surpassed, Yet Goes Unnoticed

Timestamps:
00:00 "AI Insights and Predictions"

04:02 $1.5B Anthropic Copyright Settlement

08:19 "Rise of AI Influencers"

11:37 "Google's Experimental App Generator"

14:05 "Reasoning Models and Enterprise Growth"

17:35 Trump's AI Policy Shift

22:32 "Narrow AI Revolution in Progress"

24:42 "Memory Advancements in Technology"

30:20 "Mixture of Experts Explained"

32:15 "Model Fusion Beats Best Score"

36:22 "AGI Wins IMO Gold"

37:40 "Testing AI IQ Progression"

43:07 "GPT-5.2: Human-Level Expertise"

44:17 "AI Outperforms in Valuable Work"

Keywords:
2025 AI roadmap, AI predictions, Artificial General Intelligence, AGI, AI copyright case, Anthropic lawsuit, $1.5 billion settlement, OpenAI and Disney partnership, AI influencers, AI avatars, user generated content, UGC, Lil Mikaela, AI influencer industry, TikTok Symphony Gen AI, Meta AI ad tools, HeyGen, Synthesia, Hour One, Veo, Sora, Cameos, nontechnical AI development, vibe coding, Andrej Karpathy, Gemini Canvas, ChatGPT Canvas, Claude artifacts, Cursor, Windsurf, Antigravity, Google Codex, AI Studio, low code AI tools, reasoner wrappers, business agency layer, agent frameworks, virtual machines, cloud PCs fo

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Don't lie -- when you open ChatGPT, you're looking for a quick copy-and-paste solution. 

We've all been there. 

What if I told you that was kinda the worst way possible to use some of the world's most powerful technology. 

Spoiler alert: it kinda is. 

Make sure to catch today's episode for some creative frameworks to change how you use LLMs. 

Creative Frameworks for Problem-Solving with Generative AI -- An Everyday AI Chat with Jordan Wilson 


(PS - Was hoping to have the PT. 2 Roadmap review ready to go, but feeling under the weather. Pt 2 will drop soon!)

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Topics Covered in This Episode:

1. Creative Frameworks for Generative AI Problem Solving
2. Human Agency vs. AI Efficiency Debate
3. Using Large Language Models as Thought Partners
4. Prompt Engineering vs. Context Engineering Evolution
5. Generic Parts Technique for Abstract Thinking
6. SCAMPER Framework for Business Innovation
7. Human-AI Collaboration: Enhancing Creative Outputs
8. Overcoming Cognitive Biases in AI-Assisted Strategy

Timestamps:
00:00 "Using AI as Thought Partner"

04:06 "AI's Role in Creative Problem-Solving"

08:13 "Broadening Problem-Solving Approaches"

13:11 "Using Multiple Tools for Insight"

15:07 "SCAMPER Framework Explained"

17:32 "Rethinking Problems with SCAMPER"

22:24 "Embracing Difference in Expertise"

24:45 "Building Creative Confidence"

Keywords:
Generative AI, problem solving frameworks, creative frameworks, large language models, AI thought partner, augmenting human intelligence, creative problem solving, prompt engineering, context engineering, agency over output, human-AI partnership, divergent thinking, convergent thinking, psychological distance, cognitive bias, creative confidence, creative agency, generic parts technique, SCAMPER framework, substitute, combine, adapt, modify, put to another use, eliminate, reverse, creative velocity, innovation, agentic capabilities, iterative problem solving, model reasoning, human in the loop, decision making, strategy, domain transfer, abstraction, functionally fixated thinking, solution exploration, pain point analysis, authentication, friction reduction, workflow improvement, technology product management, practical AI use cases, collaborative AI techniques, business problem solving, structured problem solving, metacognition, challenging output quality,

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We don't do AI predictions lightly. 

Everyday AI is trusted by millions each year to help guide them through the muddy AI waters. 

So in the same way you'd want transparency out of your AI models, we're rolling back the clock on our January 2025 AI predictions we dished with our Roadmap Review. 

We're busting out the receipts. 

At the time, these AI predictions seemed nutty. So, did we lead you astray? Or, did we pave the road to paydirt? 

Let's dive in y'all. 

2025 AI Roadmap Rewind: From AI Agent Orchestrators to Deepfakes. The New Tech Order -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

1. AI Agent Orchestrators as Job Title
2. AI Agents in Company Hiring Trends
3. Enterprise Reasoning Data Collection Growth
4. AI Driving Professional Services Pricing Crisis
5. Universal Basic Income and AI Job Loss
6. Open Source AI Models Surpassing Proprietary
7. Chinese AI Model Global Market Impact
8. Perplexity Answers Engine Business Pivot
9. Frontier AI API Price Drops
10. VC Funding Surge in Embodied AI
11. Advancements in AI Video Generation Tools
12. AI’s Disruption of Traditional Internet Models
13. Social Media Deepfake Misinformation Surge

Timestamps:
00:00 "2025 AI Predictions Explained"

04:02 "2025 AI Predictions Insights"

08:49 AI Agents Classified as Employees

11:23 "Transformers vs. Reasoning Models"

16:15 AI Impact on Consulting Jobs

18:51 "AI Impacts Jobs, Spurs UBI"

23:12 Open Models Surge in AI

27:13 "Perplexity's AI Pivot Journey"

29:23 "Embodied AI Sector Growth"

31:36 "Embodied AI Transforms Logistics"

36:42 "AI-Driven Internet Future"

39:16 Deepfake Fraud Crisis Escalates

42:25 AI Success Roadmap Revealed

Keywords:
Agent orchestrators, AI agent orchestration, AI job titles, orchestration engineer, AI agent architect, human-AI collaboration leader, Adobe agent orchestrator, AO, AI agents, digital FTEs, digital full time employees, AI agent hiring, agent compute costs, Agent Force, hiring digital labor, reasoning models, company reasoning data collection, reasoning token consumption, synthetic data generation, private reasoning engines, unstructured data, proprietary data, professional services pricing crisis, law sector AI disruption, consulting AI transformation, accounting

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Weird week in AI. 🤪

OpenAI partners with..... Disney? Goofy. 

Meta might go ..... close source? Avocado. 

And Adobe's brining Photoshop to..... ChatGPT? Print that. 

Tons of movement. 
We've got you covered with this week's AI News That Matters. 

OpenAI releases GPT-5.2 and teams up with Disney, Trump signs order to stop states from regulating AI, big AI rivals team up and more.

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Topics Covered in This Episode:

1. Disney Invests $1B in OpenAI Sora Partnership
2. Disney Grants OpenAI Access to Copyrighted Characters
3. Trump Executive Order Blocks State AI Laws
4. AI Regulation: Federal vs. State Controversy
5. Meta Shifts Llama Model Open Source Strategy
6. OpenAI CEO Teases Upcoming ChatGPT Features
7. Google Releases Gemini Deep Research Agent
8. Adobe Photoshop Integrates with ChatGPT Apps
9. Agentic AI Foundation Unites Top AI Rivals
10. OpenAI Launches GPT-5.2 Model Benchmarks
11. GPT-5.2 Performance: Spreadsheets & Presentations
12. Long-Context AI Model Retrieval Improvements
13. Anthropic Claude Agents and Accenture Partnership
14. OpenAI, Google, Anthropic Update Agent Frameworks

Timestamps:
00:00 "Everyday AI: News & Insights"

04:08 Disney's AI Integration & Legal Moves

07:15 AI Regulation: Federal vs State

10:30 "Meta Tightens Grip on AI"

14:57 "Google Dominates AI Year-End Race"

20:39 "Photoshop Meets ChatGPT Integration"

23:50 "Agentic AI Collaboration in Tech"

25:07 "Agentic AI & OpenAI GPT-5.2"

30:48 "Four Needles Benchmark Analysis"

32:07 "Improved Memory in AI Models"

35:23 "AI Tools & Industry Updates"

38:56 "Everyday AI: Subscribe & Connect"

Keywords:
GPT-5.2, OpenAI, generative AI, AI model, AI regulation, Trump executive order, federal AI law, state AI laws, Disney AI partnership, Sora video generator, Disney OpenAI investment, Star Wars AI content, Marvel AI content, copyright infringement,

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AI investments? Everywhere. 

AI training? ....... 🔭

Why are enterprises so quick to throw insanely large stacks of cash at any AI project or software, but neglect ongoing employee training and education? 

And what should we do about it? 

Join us to find out. 

The AI Training crisis: Why companies are spending money on AI but not educating -- An Everyday AI Chat with Jordan Wilson and Lucid Software's Dan Lawyer

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Topics Covered in This Episode:

1. The AI Training Crisis in Enterprises
2. Company Investment in AI vs. Employee Education
3. Cultural Shifts Required for AI Adoption
4. Importance of Domain-Specific AI Training
5. Grounding Large Language Models With Company Data
6. Process and Workflow Documentation for AI Success
7. Rapid Experimentation With New AI Models
8. Measuring ROI and Outcomes of AI Training
9. Creating Cultural Moments for AI Learning
10. Preparing Employees for Fast AI Technology Changes

Timestamps:
00:00 AI Training Crisis in Business

05:32 Generative AI and Family History

07:55 Grounding AI for Effective Use

11:49 "AI as a Co-Collaborator"

16:38 "Grounding AI for Better Trust"

19:29 "AI Needs Procedural Knowledge"

22:33 "Measuring AI's Value and Speed"

23:49 Rapid Experimentation and Continuous Learning

26:58 "Maximizing AI Through Education"

Keywords:
AI training crisis, AI education gap, enterprise AI adoption, AI in business, employee AI training, workplace AI integration, generative AI, large language models, AI-powered workflow, cultural shift with AI, organizational AI culture, domain specific AI training, AI-driven process documentation, AI experimentation, AI safety, securing company data with AI, best practices for AI implementation, nondeterministic AI, grounding AI in company data, procedural knowledge for AI, AI as coworker, AI collaboration, AI trust building, AI productivity, training AI like employees, measuring AI ROI, quantifiable AI outcomes, AI-powered decision making,

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Algorithms and automations have been buds for a decade plus. 🤝

But the old 'smart' automations were rigid. If one thing was wrong, the automation would bust. 

But with LLM-powered agents? Those automations are different. If something's wrong, the agent might just..... guess. 😳

Weak data = weaker outcomes. 

Here's how to fix it when agents come first and they're gonna finish the job, whether the data is strong or not. 

From Automation to Agents: Why Weak Data Makes AI Guess -- An Everyday AI chat with Jordan Wilson and Ed Macosky

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Topics Covered in This Episode:

1. Automation vs. Agentic Workflows: Key Differences
2. AI Agents: Data Quality and Output Risks
3. Agentification Trends in Enterprise Automation
4. Pros and Cons: Converting Automations to Agents
5. AI Agents Impact on Business Processes
6. Importance of Data Governance in AI Agents
7. AI Agent Control Towers and Observability
8. Measuring ROI: AI Agents and Data Investments

Timestamps:
00:00 "Trusting AI for Business Growth"

06:31 "Agent-Based Automation: Pros & Cons"

08:29 "AI Agents Simplify Workflow Management"

10:40 Expense Report Workflow Frustrations

15:05 AI Governance and Data Integrity

19:04 "Governance and Multi-Agent Data Strategy"

22:50 "AI ROI and Data Focus"

26:01 AI Studio: Create Apps Faster

Keywords:
AI agents, Agentification, Agentic workflows, Automation, AI-powered automation, Business process automation, Deterministic workflows, Non-deterministic workflows, Data quality, Data governance, Data management, Integration platforms, Boomi, Chief Product and Technology Officer, Large language models, Generative AI, Rigid workflows, Flexible workflows, Conversational agents, Expense report automation, Policy adaptation, AI decision-making, Human-in-the-loop, AI observability, AI traceability, Multi-agent orchestration, Ecosystem synchronization, Agent control tower, AI governance tools,

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Capabilities? Through the roof?

Usage? Ground floor.

Claude Agent Skills might be one of the most useful features of any front-end LLM.

Yet....it's crickets in terms of chat around it.

For this 'AI at Work on Wednesday' episode, we're breaking it down for beginners and will have you spinning up your own Claude Agent Skills in no time.

Claude Skills: How to build Custom Agentic Abilities for beginners -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

1. Claude Skills Agentic Features Overview
2. Differences: Claude Skills vs. GPTs vs. GEMS
3. Modular Agentic Workflow File Structure
4. Step-by-Step Guide: Building Claude Skills
5. Claude Skills YAML/Markdown Setup Process
6. Testing and Validating Custom Claude Skills
7. Advanced Capabilities: Executable Code & Sub-Agents
8. Common Troubleshooting for Claude Skills Creation

Timestamps:
00:00 "Claude Skill Library Unveiled"

06:27 "Claude Skills Explained"

07:29 Custom GPTs and Gems Explained

11:18 Claude Skills vs Projects

17:31 "Refining Skill Triggers Effectively"

20:17 "Beginner Cloud Skills Best Practices"

23:39 "Preferring GPT and Memory Tools"

25:54 "Saving Skill File Properly"

28:09 Creating Skills on Claude

33:43 "Creating AI News Searcher"

35:36 Claude Skills Now Available

37:39 "Optimizing Claude for Knowledge Tasks"

41:05 "Skill Builder Library Access"

Keywords:
Claude skills, Claude agent skills, custom agentic abilities, large language model, agentic workflows, specialized tasks, coding capabilities, file creation, executable code, skills library, skill builder, skill creator, markdown file, skill.md, folder structure, YAML front matter, composable skills, modular instructions, automation, prompt engineering, skill triggers, skill testing, advanced features, API skill versioning, governance and efficiency,

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OpenAI is (reportedly) in full panic mode. 🚨

All hands on deck, Code Red status.

So.... what happened?

How did OpenAI go from defining the AI category to getting beat by competitors they once trounced?

And, is it too late for them to turn it around? Or will Google permanently take the AI crown?

Tune in... we've got hot takes.

OpenAI's Code Red: Is Google taking ChatGPT's Crown? An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

1. OpenAI "Code Red" Panic Explained
2. Google Gemini 3 vs ChatGPT Benchmarks
3. November AI Model Release Impact
4. Anthropic Claude Opus 4.5 Coding Leader
5. OpenAI Financial Strain & Revenue Analysis
6. Market Share Shift: ChatGPT vs Gemini
7. ChatGPT Default Model Quality Critique
8. Enterprise AI Adoption: OpenAI vs Anthropic
9. OpenAI Ads Controversy and User Backlash
10. App Stickiness and Query Length Data

Timestamps:
00:00 "Everyday AI for Business Leaders"

07:23 AI Leadership Questioned

09:28 "Before and After ChatGPT"

12:36 "OpenAI's Shift and Competition"

15:58 OpenAI's Massive Infrastructure Growth

21:13 "Google Surges, Challenges OpenAI"

23:53 "Cloud Ovis 4.5 Review"

28:45 OpenAI Criticized Over Ad Controversy

31:36 "ChatGPT's Default Model Critique"

33:58 "Optimize Your Chat Model Usage"

35:49 "Choosing GPT-5 Over Gemini 3"

40:20 AI Narrative Overblown in Tech

43:57 "AI Tools and Code Red"

45:24 "Build Apps with AI Studio"

Keywords:
OpenAI, code red, ChatGPT, Google Gemini, Gemini 3, AI race, large language models, Anthropic, Claude Opus 4.5, AI leadership, market share, AI benchmarks, LLM development, GPT-5.1, thinking models, default chat model, model performance, user growth, AI chatbots, reasoning multimodality, enterprise AI adoption, CapEx spend, custom AI chips, Broadcom, Nvidia,

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OpenAI has launched a code red. 🚨

After increasing pressure from Google, OpenAI is reportedly in ‘all hands on deck’ mode to reclaim the LLM crown. 

Meanwhile, Google quietly released an EVEN MORE powerful version of Gemini 3 that hardly no one noticed. 

And Perplexity? They got hit with a massive lawsuit. 

You take one week off of AI, and you could be months behind if you’re in a fast-moving industry. Don’t sweat… we do the heavy lifting for you. 

Join us for our weekly ‘AI News That Matters’ segment, your weekly shortcut to save you hours on the AI happenings. 

OpenAI’s Code Red, Google’s Deep new model, Perplexity facing big lawsuit and more — An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

1. Google Gemini 3 DeepThink Model Launch
2. Perplexity Lawsuit by New York Times, Tribune
3. Amazon AWS Nova 2 AI Model Family
4. Accenture Deploys ChatGPT Enterprise Partnership
5. Anthropic Prepares for $300B IPO
6. Google Workspace Studio No-Code AI Agents
7. Google and Replit AI Coding Partnership
8. OpenAI Code Red: GPT-5.2 Model Release Rumors

Timestamps:
00:00 "Gemini 3 Deep Think Rollout"

05:31 "Perplexity Sued Over Copyright Violations"

09:04 "Amazon's NovaForge Enterprise AI Service"

10:56 Accenture Adopts ChatGPT Enterprise

15:59 OpenAI vs. Anthropic: Profitability Debate

17:46 "Google Workspace Studio Automation"

22:07 "OpenAI's Code Red Response"

23:18 "OpenAI Shifts Focus Amid Competition"

26:44 "Google's Big November Wins"

31:06 "2025 AI Deals & Outlook"

Keywords:
Gemini 3, Gemini 3 Deep Think, Google AI, powerful reasoning model, parallel reasoning, large language models, GPT QA Diamond, Arc AGI 2, code execution, AI Ultra subscribers, advanced benchmarks, complex math problems, AI outages, OpenAI, code red, GBT 5.1 Pro, ChatGPT 5.1 Pro, Anthropic, Claude 4.5, consulting adoption of GenAI, Accenture ChatGPT enterprise, enterprise generative AI, copyright lawsuit, Perplexity AI, New York Times lawsuit, Chicago Tribune lawsuit, news content copyright, scraping paywalls, AWS re:Invent, Amazon AI models, Nova 2 family, Nova 2 Lite, Nova 2 Pro, Nova 2 Sonic, Nova 2 Omni, multimodal reasoning, NovaForge, Novellas, customized AI models, enterprise cloud AI, AI consulting trends, outcomes-based consulting, Workspace Studio

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How can you scale AI at the enterprise, yet still hit your climate goals? 

And can heavy AI usage and an enterprise's ESG mission co-exist? 

Ashutosh Ahuja lays it out for us. 

Aligning AI With Climate And Business Goals -- An Everyday AI Chat with Jordan Wilson and Ashutosh Ahuja

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Topics Covered in This Episode:

1. AI's Environmental Impact and Climate Concerns
2. Companies Aligning AI with ESG Goals
3. AI Adoption Versus Carbon Footprint Tradeoffs
4. Metrics for Measuring AI's Environmental Impact
5. Business Efficiency Gains from AI Adoption
6. Real-World Examples: AI Offsetting Carbon Footprint
7. Industry Opportunities for Sustainable AI Integration
8. Future Trends: Efficient AI Models and Edge Computing

Timestamps:
00:00 Everyday AI Podcast & Newsletter

05:52 Balancing Progress and Legacy

07:03 "Should Companies Limit AI Usage?"

12:02 "Sentiment Analysis for Business Growth"

17:07 "Energy Efficiency Impacts ESG Metrics"

19:40 Robots, Energy, and AI Opportunity

21:41 AI Efficiency and Climate Balance

25:04 "Trust Instincts in Investments"

Keywords:
AI and climate, climate goals, aligning AI with ESG, environmental impact of AI, carbon footprint, energy use in AI data centers, water cooling for GPUs, sustainable business practices, enterprise AI strategy, ESG compliance, climate pledges, AI adoption in business, carbon footprint metrics, machine learning for sustainability, predictive analytics, ethical AI, green AI solutions, renewable energy sector, AI in waste management, camera vision for waste sorting, delivery robots, edge AI, small business AI implementation, AI efficiency, sentiment analysis, customer patterns, predictive maintenance, IoT data, auto scaling, cloud computing, resource optimization, SEC filings, brand sentiment tracking, LLM energy consumption, environmental considerations for AI, future of AI in climate action, business efficiency, human in the loop, philanthropic business practices, sustainable architecture, large language models and climate, tech industry climate initiatives, AI-powered resource savings, operational sustainability.

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