What if "designing well" no longer requires years of training, and the real work becomes choosing the story to tell?

In this episode of Artificial Insights, host Daniel Manary interviews Ahmad Iqbal, product lead and Head of MENAP at Canva, about what it really means to design for accessibility in the age of AI. Ahmad helped launch Canva’s first generative AI features and shares how the company's guiding principle, making creativity accessible to everyone, drives its product, culture, and internal AI adoption.

The conversation traces how a 48-hour hackathon project became Magic Write, how Ahmad approached AI as a non-technical product manager, and why the best ideas at Canva don’t ship if they’re not simple enough for anyone to understand.

The episode closes with a wider reflection on how AI changes not just what we design, but who gets to create. Drawing on Marshall McLuhan’s writing, Ahmad describes a future where AI lowers the skill floor, turning consumers into producers and expanding who can participate in design and storytelling.

🔑 What You’ll Learn in This Episode

• ✅ Why "accessibility" is Canva’s north star for AI features
• ✅ How a 48-hour hackathon prototype became a core product surface
• ✅ What "truly local" means for regional expansion and product fit
• ✅ How to encourage teams to use AI internally to unlock time for harder problems
• ✅ Why McLuhan’s "the medium is the message" matters for AI-era design

🔗 Resources & Links

• 🌐 Explore Canva: https://www.canva.com
• 📖 Read about Marshall McLuhan: https://en.wikipedia.org/wiki/Marshall_McLuhan
• 🎧 Check out the Student Specials from S3: https://rss.com/podcasts/manaryhaus/2185718/
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💬 Building AI into real products or localizing at scale? Share this episode with a PM or regional lead who will benefit from Ahmad’s playbook.

Most people talk about "data privacy" like it’s a legal checkbox. For Josh Müller, it’s part of faithful stewardship.

In this bonus episode, Josh and Daniel pick up on a single idea from their main conversation: data management as Kingdom stewardship. Josh shares how his once “off-the-cloud-at-all-costs” mindset softened as AI made it possible to do genuinely good things with data.

The hard part now isn’t the tech. It’s building the habits and structures that keep data private, local, and used for good.

💡 Want the full conversation?

This clip comes from Josh Müller’s conversation with Daniel, where they dive into AI-powered translation, offline models, and how Waha uses automation to multiply impact across 43 languages.

Listen to the full episode: https://rss.com/podcasts/manaryhaus/2282863/

🔗 Resources & Links

• 🤝 Connect with Josh on LinkedIn: https://www.linkedin.com/in/thejoshmuller/
• 🌐 Explore Waha: https://waha.app/
• 🌐 Josh’s site: https://joshmuller.ca/
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💬 Enjoyed this one? Share it with a builder wrestling with data stewardship and privacy.

What if a six-month production bottleneck became a 16-minute job?

Josh Müller, director at tech nonprofit Waha, tells Daniel how forced alignment and smart automation turned a tedious audio-editing gauntlet into a repeatable pipeline that ships Bible study content across dozens of languages. The result is more time for actual disciple-making and less time in Audacity.

They dig into privacy-first analytics in risky contexts, when to choose local models over cloud tools, and why data management is Kingdom stewardship.

🔑 What You’ll Learn in This Episode

• ✅ How timestamping with forced alignment collapsed months of manual audio work into minutes
• ✅ Practical ways nonprofits can use agents and local LLMs without leaking sensitive data
• ✅ A privacy posture that starts with “we can’t leak what we don’t have”
• ✅ When to reach for tools like Whisper for offline transcription, and agentic coders for data chores
• ✅ Why disciplined data stewardship unlocks future AI use while protecting people at risk

🔗 Resources & Links

• 🤝 Connect with Josh on LinkedIn: https://www.linkedin.com/in/thejoshmuller/
• 🌐 Explore Waha: https://waha.app/
• 🌐 Josh’s site: https://joshmuller.ca/
• 📚 Meta’s Massively Multilingual Speech (forced alignment): https://ai.meta.com/blog/multilingual-model-speech-recognition/
• 💻 OpenAI Whisper (speech-to-text): https://github.com/openai/whisper
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💬 Enjoyed this one? Share it with a builder wrestling with data stewardship and real-world multilingual workflows.

When is the right time to start a company in AI? Adeel Zaman argues it comes down to breakthroughs.

In this bonus clip, Adeel explains why "why now" matters more than ever in AI. Without a recent step forward, most ideas have already been tried and blocked by the same old limits. He points to Jeff Bezos in 1995, who spotted the internet’s growth curve and built Amazon around books, an application that fit the technology’s strengths at the time.

🎧 Curious where Adeel takes this next?

In the full conversation, he and Daniel talk about building embodied intelligence, why voice feedback may be the missing ingredient in reinforcement learning, and how personalized AI could change both privacy and performance.

Listen to the full episode: https://rss.com/podcasts/manaryhaus/2238950

🔗 Resources & Links

• 🤝 Connect with Adeel on LinkedIn: https://www.linkedin.com/in/adeelzam/
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💡 If this sparked a thought, share it with a founder or builder who’s weighing their own "why now" moment.

What happens when AI doesn’t just live in text and screens, but begins to reason and act in the physical world?

Adeel Zaman, CTO and Co-Founder of DOZR, has spent his career moving from deep learning research to startups, scaling companies and tackling cold-start problems with machine learning. Now, backed by the HF0 residency, his focus is on "Embodied Intelligence" and how foundation models can learn physical tasks, adapt through feedback, and interact with humans in real time.

In this conversation, Daniel and Adeel explore why embodied AGI may be a prerequisite for true general intelligence, how voice interaction could change human-machine collaboration, and what it means to give individuals, not just big labs, the ability to teach and shape their own AI models.

🔑 What You'll Learn in This Episode

• ✅ Why embodied intelligence could be a prerequisite for reliable long-horizon agents and true "AGI"
• ✅ How real-time "reasoning out loud" can change human-robot collaboration on job sites
• ✅ Why reinforcement learning from language feedback is key when rewards aren't cleanly verifiable
• ✅ The case for individually owned AIs vs. globally shared weights

🔗 Resources & Links

• 📄 Google DeepMind’s RT-2 research overview: https://robotics-transformer2.github.io
• 🤝 Connect with Adeel on LinkedIn: https://www.linkedin.com/in/adeelzam/
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💬 Inspired by the idea of embodied AI? Share this episode with someone else who would be interested in the conversation!

Most people aren’t natural tinkerers. That simple fact shapes how AI adoption actually plays out inside companies. It’s not enough to drop a new tool into the workflow. Deadlines and old habits keep teams from trying new approaches.

In this bonus episode, Daniel and Aydin Mirzaee, CEO of Fellow, talk about why adoption stalls when people stick to the “old way,” how organizations can carve out space to experiment, and why patience is needed to push through the frustration curve.

It’s a candid look at what it really takes for teams to shift from tinkering to transformation.

🎧 Want the bigger picture?

This clip is just a glimpse of Aydin’s perspective. In the full episode, he shares how Fellow pivoted from manual meeting tools to an AI-first platform, why building for future model capabilities is now a necessity, and how AI as a “chief of staff” can raise the quality of decisions across an organization.

Listen to the full episode: https://rss.com/podcasts/manaryhaus/2227017/

🔗 Resources & Links

• 🤝 Connect with Aydin on LinkedIn: https://ca.linkedin.com/in/aydinmirzaee
• ✨ Fellow — AI meeting assistant: https://fellow.ai
• 📩 Subscribe to the Artificial Insights newsletter for summaries and takeaways: https://manary.haus/podcast/#haus

🚀 Learn something new? Leave a review and share it with someone building in AI!

Adopting AI inside an organization is rarely smooth. Most people are not natural tinkerers, and it takes more than enthusiasm to change how teams prepare, run, and follow up on meetings.

For Aydin Mirzaee, CEO and co-founder of Fellow, the turning point was realizing that AI could take the work only the most organized people were willing to do and make it accessible to everyone. He calls this an AI “chief of staff”, a system that prepares meaningful follow-ups, captures decisions and actions, and threads context across meetings so teams can focus on higher-value work.

In this episode, Daniel and Aydin explore what it looks like to move from manual meeting hygiene to AI-first workflows. They discuss reasoning-driven pre-meeting briefs, role-specific templates, and writing back into systems like Salesforce and Jira without extra human effort. Aydin also reflects on adoption realities: how to create room for experimentation, why surfacing high-value workflows matters more than tinkering, and what it means to build for models that don’t exist yet.

🔑 What You Will Learn This Episode

• ✅ Why summaries are table stakes and the real gains come from workflow design
• ✅ How meeting-type templates help you capture the right signals
• ✅ What it takes to drive adoption when most people are not tinkerers
• ✅ Why builders need to build toward a model’s future capabilities, rather than current ones

🔗 Resources & Links

• ✨ Fellow — AI meeting assistant: https://fellow.ai
• 🤝 Connect with Aydin on LinkedIn: https://ca.linkedin.com/in/aydinmirzaee
• 🎧 This New Way with Aydin Mirzaee: https://www.youtube.com/@aydin.mirzaee
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus

👉 If this conversation sparks an idea, share it with a colleague! It’s a grounded look at how AI reshapes work inside real teams.

Welcome to Artificial Insights where we talk to leaders and thinkers in AI about how to do AI right. On this podcast, we sit down every other Friday with people who build things with, and write things about, AI, and talk to them about what they do and why they do it.

We've been doing this for just over a year now, and a core pattern has emerged:

Why you build with AI matters as much as how you build with AI.

Join Daniel this season as he meets with and learn from a great lineup of guests from awesome companies like Canva, Fellow.ai, and Waha.

We can't wait to introduce them all to you!

In the meantime, check out some of our favorite episodes!

• 🎧 Season 1, Episode 8 with Kris Braun on why human expertise and judgement remain essential, even with AI in the loop: https://rss.com/podcasts/manaryhaus/1788572/
• 🎧 Season 2, Episode 1 with Patrick Belliveau on how curiosity, persistence, and early GPT models kickstarted an unexpected AI journey: https://rss.com/podcasts/manaryhaus/1875537/
• 🎧 Season 2, Episode 6 with Mike Kirkup on what it’s like to demo an AI system you can’t fully predict: https://rss.com/podcasts/manaryhaus/2061508/
• 🎧 Season 3, Episode 2 with Dr. Christopher Watkin on what “infinite efficiency” forces us to rethink about work, effort, and meaning: https://rss.com/podcasts/manaryhaus/2167772/

🔗 Resources & Links

• 🎧 Listen to more episodes: https://rss.com/podcasts/manaryhaus/
• 🤝 Connect with Daniel on LinkedIn: https://www.linkedin.com/in/dmanary/
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

🚀 Share this trailer with a friend who’s just starting their AI journey. Thanks for listening!

From ethics to student builders, this past summer season of Artificial Insights dug into how AI shapes what it means to be human and how the next generation is already learning to use it.

Guests shared warnings about convenience, reflections on human worth, and hands-on lessons from shipping early projects.

In this recap episode, Daniel looks back at highlights from every conversation:

• 1️⃣ Sheldon Fernandez on the “instantaneous friend,” useful friction, and raising wise humans. Listen to the full episode: https://rss.com/podcasts/manaryhaus/2157598/
• 2️⃣ Dr. Christopher Watkin on efficiency, meaning, and why friction still matters. Listen to the full episode: https://rss.com/podcasts/manaryhaus/2167772/
• 3️⃣ Dr. K on the wisdom gap, the tyranny of convenience, and protecting agency. Listen to the full episode: https://rss.com/podcasts/manaryhaus/2178635/
• 4️⃣ Dvir Zagury on aligning personalization with privacy and control. Listen to the full episode: https://rss.com/podcasts/manaryhaus/2182186/
• 5️⃣ Aleks Santari on commoditized intelligence and adaptive, human-centred design. Listen to the full episode: https://rss.com/podcasts/manaryhaus/2185753/
• 6️⃣ Aasha Khan on safe spaces for students to learn AI and building with purpose. Listen to the full episode: https://rss.com/podcasts/manaryhaus/2185718/

It’s a chance to revisit the season’s biggest insights, and a reminder that doing AI right starts with protecting human worth, agency, and learning.

🔑 What You’ll Learn in This Episode

• ✅ Why adding friction can protect judgment and trust
• ✅ How to decide which decisions must remain human
• ✅ Patterns for privacy, consent, and editable context
• ✅ Lessons from the next generation about how AI should be and can be used

🔗 Resources & Links

• 🤝 Connect with Daniel on LinkedIn: https://www.linkedin.com/in/dmanary/
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

🎧 Looking back sharpens how we move forward. Share this episode with someone reflecting on AI’s role in their own work.

What happens when students are told not to use AI, but also told they’ll need it for their careers?

For Aasha Khan, a Grade 12 student at Cameron Heights and founder of Youth Tech Labs, that tension defined her first encounters with AI. At school, the message was clear: avoid AI or risk suspension. At home, her father, a Chief AI Officer, encouraged her to explore the technology. The mixed signals left her, like many of her peers, caught between fear and curiosity.

Aasha decided to create a safe space where high schoolers could learn AI together. Youth Tech Labs has since grown into a community that draws more than a hundred students, runs hands-on workshops, and hosts demo days where participants present their AI projects. Along the way, Aasha launched AskEve, an AI chatbot designed to break stigma around menstruation and open up conversations often kept silent.

This is the third and final part of our Back to School Special. If you missed them, check out episode four with Dvir Zagury on how curiosity led him from quantum foundations to health tech and personalized AI here: https://rss.com/podcasts/manaryhaus/2182186/ and episode five with Aleks Santari on how AI can fundamentally change the way interfaces are designed here: https://rss.com/podcasts/manaryhaus/2185753

🔑 What You’ll Learn in This Episode

• ✅ Why unclear rules around AI leave students confused and divided
• ✅ How Youth Tech Labs helps students build real projects in a supportive environment
• ✅ Why empathy and creativity are central to student-led AI initiatives
• ✅ How projects like Ask Eve show AI’s potential for social good
• ✅ Why parents and teachers need to create safe spaces for youth to explore AI

🔗 Resources & Links

• 🤝 Connect with Aasha on LinkedIn: https://www.linkedin.com/in/aasha-khan-3a2294250/
• 🌐 Check out Youth Tech Labs: https://youthtechlabs.ca
• 🌐 Check out Gambit Changemakers: https://gambitco.io/#changemakers
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

🎒 Inspired by this Back to School Special? Share it with a parent, teacher, or student curious about how AI will shape the classroom and beyond.