In this teaser, Jeremy and Henrik debrief their conversation with Diarra Bousso, founder of the AI-first fashion startup DIARRABLU. They reflect on Diarra’s use of the word “yet” as a signal of growth, what it means to run a fashion brand more like a lab, and how her team “manages her back” when the ideas overflow. They also explore how AI is reshaping speed, sustainability, and experimentation in the fashion industry, and why your own lived experience might be your biggest asset in an AI-powered world.
Full episode dropping next week.

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

In this episode, Illia Polosukhin joins Henrik and Jeremy to trace the origins of transformers and how practical constraints inside Google led to a breakthrough that reshaped modern AI. He explains why recurrent models were hitting limits, how parallel attention opened the door to scale, and why he believed a major jump in capability was imminent long before the rest of the world saw it.

The conversation then turns to the risks and responsibilities of today’s AI systems. Illia describes how models can be subtly guided to influence user opinions, why open weights are not the same as truly open models, and how hidden behaviors can be embedded during training. He explains why provenance and verifiable data pipelines matter, especially as AI begins mediating more of the information we rely on.

Later in the episode, Illia outlines how blockchain can support trust, identity, and coordination in a future where AI agents act on our behalf. He shares why information is becoming more valuable than money, how ownership of personal AI models will shape user agency, and why domain expertise becomes significantly more powerful when paired with modern generative tools.

Key Takeaways:

• Transformers emerged from practical constraints, not theory
  Illia explains that the shift from recurrent networks to attention was driven by speed and parallelization needs at Google, not a desire to invent a new paradigm.
• AI’s step change was foreseeable to early builders
  Illia expected a ChatGPT level breakthrough several years before it arrived, based on clear research signals and accelerating model performance.
• Provenance and trust will define the next phase of AI
  As AI systems can be subtly manipulated, Illia argues that verifiable data pipelines and transparent training processes are essential to prevent large scale misinformation.
• Ownership and identity matter in an agent driven world
  Illia believes individuals will soon rely on AI agents that act autonomously, making it critical that users own their models and that interactions between agents are secured and verified.

https://near.ai – NEAR AI Cloud and Private Chat products are now live, try them here
Illia's X: x.com/ilblackdragon
Illia's Substack: ilblackdragon.substack.com
NEAR X: x.com/nearprotocol

00:00 Intro: AI and Information Control
00:29 Meet Illia Polosukhin: Co-Author of 'Attention is All You Need'
01:03 The Evolution and Impact of AI
13:24 The Birth of Near AI and Blockchain Integration
15:16 Challenges and Innovations in Blockchain and AI
22:17 Privacy and Security in AI Applications
26:58 Exploring Sleeper Agents in AI
29:19 Practical AI Implementation in Teams
30:06 AI's Role in Product Development
31:41 Challenges and Future of AI in Development
36:35 AI and Economic Alignment
41:46 The Future of AI Agents
44:14 Debrief

📜 Read the transcript for this episode: Transcript of The Future Of AI With Illia Polosukhin: The Man Who Put The T In GPT |

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

In this teaser, Jeremy and Henrik reflect on their conversation with Illia Polosukhin, co-author of the “Attention Is All You Need” paper and founder of Near Protocol. They dig into Illia’s early expectations for ChatGPT, why “owning your AI” isn’t just a catchphrase, and how blockchain could help protect the information we rely on. They also explore what it really means to work with AI and why your own experience might be more powerful than you think.
Full episode dropping soon.

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

In this episode, Christian Keller joins Henrik and Jeremy to explain how world models are shaping the next stage of generative AI. He talks through how AI learns using different types of inputs, and why video adds a sense of continuity, change, and cause and effect that text alone does not provide. Christian shares vivid analogies and clear examples to show what multimodal models make possible.

The conversation moves into how AI is now used throughout the research process, from generating synthetic data to evaluating model outputs. Christian shares how this loop is already in motion and how AI is helping scale and accelerate experimentation. He also reflects on the shift after ChatGPT launched, and how that changed the pace and structure of research work.

Later in the episode, Christian describes how individual workflows are evolving, and how asking simple questions like “Could AI help with this?” often opens new possibilities. He shares examples from his own work and home life, including how his wife built and graded her own French exercises using generative tools.

Key Takeaways:

• Text removes essential information
  Christian explains that text compresses reality and loses detail, context and temporality. Images and video help restore what text leaves out.
• World models give AI a sense of change
  Video introduces the before and after and how things move or enter a scene. This helps models learn cause and effect and builds more robust understanding.
• AI helps build AI
  Models can generate data, evaluate results and support researchers during development. Christian shows how this creates new ways of scaling experimentation and training.
• Workflows shift when AI handles early steps
  Christian shows how tasks like debugging and prototyping change with generative tools, which reshapes roles and opens new opportunities for innovation.

LinkedIn: Christian Keller | LinkedIn

00:00 Intro: Information Compression
00:37 Meet Christian Keller: AI Expert
01:13 The Evolution of AI Products
02:11 Impact of ChatGPT on AI Development
02:38 Understanding PyTorch and Its Role
07:41 The Bitter Lesson in AI
09:12 Challenges and Future of AI Models
18:57 Using AI to Build AI
23:25 Innovative Chat Interfaces
23:41 Building the Autos Platform
24:35 Epiphanies in AI Integration
25:18 AI in Entrepreneurial Workflows
26:32 Challenges in AI Integration
31:15 Bias in AI Models
38:06 Debrief

📜 Read the transcript for this episode: Transcript of AIs Next Frontier: World Models Explained by Christian Keller |

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

In this teaser, Jeremy and Henrik break down their immediate takeaways from their conversation with Christian Keller, including model fidelity, hallucinations, and the surprising ways AI is already reshaping everyday workflows.
Full episode drops tomorrow.

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Generative AI is moving fast, but most organizations aren’t. Tim Creasey and Paul Gonzalez have spent their careers studying why. As leaders at Prosci, they’ve worked with thousands of teams navigating complex change, and in this episode they share what their research says about the human side of transformation.

They discuss why traditional tactics like comms and training break down in the face of rapid AI adoption, and how successful organizations create the conditions for people to actually change. From hands-on leadership and peer-driven learning to the power of experimentation and the ADKAR model, this conversation is packed with practical tools and hard-earned insights.

Tim and Paul also explore how AI is reshaping organizational structures, what “exposure hours” reveal about executive readiness, and why culture beats mandates every time. Whether you’re leading change or stuck inside it, this episode offers a grounded look at what actually works when everything is in motion.

Key takeaways:

• Bold vision is not enough - it also needs to be balanced
  The most effective AI leaders communicate both where the organization is going and what teams are doing right now to get there. Prosci’s research shows that near-term clarity matters just as much as long-term ambition.
• Leaders need to use the tools themselves
  Tim and Paul introduce the idea of “exposure hours” as a leading indicator of readiness. The more time executives spend actively experimenting with AI, the better positioned they are to lead transformation.
• Experimentation requires structure and safety
  Organizations can’t just tell people to try new things. They need to carve out time, reduce the stakes, and make experimentation a shared and visible part of how work gets done.
• Real change still happens one person at a time
  Despite all the new tech, the fundamentals haven’t changed. Individuals need awareness, desire, knowledge, ability, and reinforcement to adopt new behaviors. Prosci’s ADKAR model remains essential for making change stick.

LinkedIn: Prosci: LinkedIn
Website: Prosci | The Global Leader in Change Management Solutions

00:00 Introduction to Change Management and AI Adoption
00:25 Meet the Experts: Tim Creasey and Paul Gonzalez
01:51 The Challenges of Change Management
04:07 Generative AI Transformation: Unique Challenges
07:44 Key Ingredients for Successful AI Adoption
15:18 Building a Culture of Experimentation
20:43 The Role of Leadership in AI Transformation
25:54 Future Organizational Designs with AI
27:02 Disruptive Organizational Changes
28:00 Examples of Innovative Enterprises
28:15 Military Analogies in Business
29:30 Challenges in Organizational Change
30:36 Timeless Principles of Change Management
31:36 The Role of Leadership in Change
33:13 ADKAR Model for Change
35:51 Addressing Resistance to Change
40:05 Effective Communication Strategies
47:48 Concluding Thoughts and Reflections

📜 Read the transcript for this episode: Transcript of How Science Suggests You Change Your Organization - with Prosci’s Tim Creasey and Paul Gonzalez |

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Applied Intuition builds the kind of AI you don’t see, but can’t live without. Co-founders Qasar Younis and Peter Ludwig share how their $15 billion company powers vehicle intelligence across cars, trucks, tanks, mining equipment, and defense systems operating in some of the most demanding conditions on earth.

They explain why combining AI with safety-critical systems raises the stakes, how a single mistake can destroy an entire company, and why so many autonomy startups ended up in the “graveyard.” The conversation explores the slow, methodical path to real autonomy, the hidden complexity of machines that run nonstop, and why consumer AI metaphors break down once software meets the physical world.

Qasar and Peter also reflect on how Applied uses AI internally, how their principle of “radical pragmatism” keeps innovation grounded, and what it takes to move fast without breaking things when lives and livelihoods are on the line. From six-figure labor shortages in remote mines to the future of defense and logistics, this episode reveals how AI is quietly transforming the physical world — one carefully coded system at a time.

Key Takeaways:

• Safety changes everything about AI
  When AI moves from the screen to the real world, the rules change. Qasar and Peter explain why building for trucks, tanks, and jets demands a different kind of discipline — one where precision and safety replace speed and iteration.
• The graveyard of autonomy is real
  There’s a long list of companies that underestimated what it takes to build safe, reliable autonomy. Applied Intuition’s founders share what went wrong — and why moving slower has been their biggest advantage.
• Radical pragmatism is the hidden differentiator
  Inside Applied Intuition, “radical pragmatism” isn’t a slogan — it’s a practice. Qasar and Peter describe how it guides product decisions, culture, and leadership, helping them innovate in places where failure isn’t an option.
• The next frontier of AI is off the screen
  From mines to military systems, the future of AI won’t be chatbots — it will be machines that think, move, and decide in the physical world. Jeremy and Henrik reflect on how that shift raises the bar for builders, leaders, and the technology itself.

Applied Intuition: http://applied.co/
LinkedIn: linkedin.com/Applied
X: https://x.com/Applied

00:00 Intro: Safety Critical Systems
00:33 Meet the Founders of Applied Intuition
01:09 Understanding Applied Intuition's Unique Approach
03:02 The Human-Machine Teaming Concept
07:26 Challenges in Autonomous Driving
16:39 AI in Industrial Applications
28:27 Future of Fighter Jets and AI
29:50 AI in Applied: Coding Tools and Beyond
33:16 Radical Pragmatism and AI Integration
36:03 Challenges of AI Adoption in Large Organizations
39:56 Human and Technical Challenges in AI
42:02 Innovation and Organizational Structure
48:38 Reflections on AI and Future Prospects

📜 Read the transcript for this episode: Transcript of You Can’t Vibe Code a 100-Ton Truck: Inside Applied Intuition’s Approach to Safety-Critical AI

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

As Head of IBM Consulting, Mohamad Ali led one of the most ambitious enterprise AI transformations to date. When he rejoined the company, he turned IBM into its own “Client Zero,” testing every idea internally before bringing it to market. The effort began with massive hackathons involving 150,000 employees, designed to turn curiosity into capability and build belief at scale.

Mohamad breaks down the three pillars that made it work: leadership that deeply understands AI, a willingness to redesign core processes, and broad employee engagement. The results were measurable and market-moving: $3.5 billion in cost savings, an eight-point business turnaround, and a doubling of IBM’s stock price.

Jeremy and Henrik unpack why IBM’s model may signal the future of consulting—organizations that act as their own laboratories for change. They reflect on how applied AI is emerging as its own discipline, where the challenge isn’t building models but re-architecting systems, workflows, and culture around them.

Key Takeaways:

• Start with Yourself: “Client Zero” Works
  IBM transformed internally before advising clients, using its own systems as a testing ground. This allowed the team to validate AI tools, workflows, and cultural shifts in real conditions, creating credibility and clarity before going to market.
• Transformation Needs More Than Tech
  Success came from a mix of technical leadership, process redesign, and cultural momentum. AI wasn’t just layered on; it was embedded into workflows, backed by leadership buy-in, and powered by 150,000 employees who participated in company-wide hackathons.
• Digital Labor Is Reshaping Business Models
  IBM didn’t just automate tasks. It redeployed 40% of HR into billable consulting roles. This shift points to a new model for consulting and services, where hybrid human plus AI teams redefine how value is delivered and monetized.
  Measure and Share the Impact
  Transformation became real when IBM tied outcomes to business metrics. By reporting $3.5 billion dollars in savings and tracking results with the CFO, IBM showed how to make AI adoption tangible, accountable, and visible to both employees and investors.

LinkedIn: Mohamad Ali - IBM | LinkedIn
IBM: IBM

00:00 Intro: HR Automation
00:41 Introduction of Mohamed Ali and IBM's Transformation
01:14 IBM's Enterprise Transformation
01:41 The Role of AI in IBM's Success
03:25 Rejoining IBM: A Strategic Decision
04:33 Key Components of AI Implementation
07:21 Employee Engagement and Hackathons
08:59 Technical Leadership and AI
10:37 Global Tax Optimization with AI
11:17 Scaling AI Solutions for Clients
22:00 Monetizing Digital Labor
26:50 Digital Labor and Procurement Projects
27:29 Unbundling and Economic Implications
28:44 Technological Shifts and Market Expansion
30:04 AI-Powered Business Transformations
32:22 Case Study: L'Oreal's AI Integration
39:13 HR Automation and Redeployment
42:09 Creative Innovations in AI Applications
43:59 Advice for Leaders on AI Integration
45:43 Final thoughts

📜 Read the transcript for this episode:

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.