Artificial Insights: Conversations About AI

Students are already finding their own ways to use AI. Can schools do a better job of showing them how to use it well?

In part 1 of our student special, Aasha argued that schools need more AI literacy and less fear.

In part 2, Keya described the tension students feel when AI is helpful and suspicion is high.

In part 3, Maizah named the deeper dilemma of living with a tool that is everywhere.

In part 4 of 4, Daniel speaks with Vaani, a high school student interested in law, coding, and the arts, whose perspective is especially practical.

Vaani uses AI in a very clear-eyed way. She finds it useful for math-heavy and physics-heavy questions, for generating practice tests, and for debugging code when she gets stuck. She believes AI should help you do your work, not do your work for you. She sees the limits of AI writing clearly. She also sees the missed opportunity when teachers allow or use AI in practice, but don't show students how to use it well.

Students are already using AI. How are schools guiding that use and can they do it with more clarity, better examples, and more honest conversation?

🔑 What You’ll Learn in This Episode

• ✅ How AI can be used for practice tests, difficult concepts, and studying outside class
• ✅ Why debugging code can be a strong example of structured AI use in school
• ✅ How some teachers encourage AI use, but leave students to figure out the details on their own
• ✅ Why there's a sharp line between AI helping with work and doing the work itself
• ✅ What schools could do differently to teach students how to use AI more wisely

🔗 Resources & Links

• 🌐 Learn more about Youth Tech Labs: https://www.linkedin.com/company/youth-tech-labs
• 📩 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]

💬 Know a teacher, parent, or school leader trying to think more clearly about responsible AI use in school? Share this episode with them.

What happens when AI is everywhere in a student’s life, but school mostly talks about it as something to avoid?

In part 1 of our student special, Aasha called for AI literacy instead of fear. In part 2, Keya described what it feels like when trust breaks down around student work. In part 3 of 4, Maizah widens the lens again: she talks about what it's like to grow up with AI as a constant presence, even while school treats it as taboo.

Maizah is a Grade 12 student and her perspective is thoughtful, conflicted, and very current. She sees how useful AI can be. How it makes schoolwork faster, helps with math, and how it is omnipresent in search, social media, and creative tools. For many students, it's already woven into daily life.

At the same time, she's asking questions that aren't easy to answer. What happens to your writing if AI keeps polishing it for you? What happens to your attention span if you stop reading deeply? What does it mean when younger siblings are growing up on AI-generated content before they can make sense of it?

This conversation stands out because Maizah isn't trying to flatten AI into a simple good-or-bad story. She's describing the real dilemma students are living with right now. AI is useful, and it is hard to escape. It raises real concerns about learning, creativity, and the kind of habits students are forming.

🔑 What You’ll Learn in This Episode

• ✅ Why AI can feel impossible for students to avoid once it becomes part of everyday life
• ✅ How AI may be shaping writing, reading, and attention span
• ✅ How schools still treat AI as taboo instead of teaching students how to understand it
• ✅ How environmental concerns are shaping the way some students think about AI
• ✅ Why AI-generated content raises new questions for younger siblings and families

🔗 Resources & Links

• 🌐 Learn more about Youth Tech Labs: https://www.linkedin.com/company/youth-tech-labs
• 📩 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]

💬 Know a teacher, parent, or school leader trying to think clearly about AI, attention, and what students are learning? Share this episode with them.

Students are already building AI into how they learn—are schools can help them use it well?

In part 1 of our student special on AI and education, Aasha raised the question of what schools are actually preparing students for. In part 2 of 4 of our student special on AI and education, Daniel speaks with Keya, a Grade 12 student balancing classes, sports, work, and plans for what comes after graduation. In this conversation, she shares a grounded and optimistic view of AI at school. For her, AI is already part of the rhythm of student life. It can explain tough concepts, generate practice quizzes, walk through calculus problems step by step, and help students study when teachers are not available.

She also describes the tension that comes with all that. Teachers may encourage AI for studying, then rely on detection tools that are far less certain when it comes to student writing. Keya tells the story of fighting for credit on an English assignment she had done herself, and how stressful that became in a Grade 12 course that mattered for university applications.

This episode is a practical look at how students are actually living with AI now, and what adults may need to understand better.

Stay tuned for part 3 of the 4-part series!

🔑 What You’ll Learn in This Episode

• ✅ How students use AI to study, practice, and understand difficult material
• ✅ Why students find AI especially helpful outside school hours
• ✅ How one false AI accusation turned into a fight for a real grade
• ✅ Where teachers are encouraging AI use, and where they are wary
• ✅ Why the biggest issue may be learning how to use AI well

🔗 Resources & Links

• 🌐 Learn more about Youth Tech Labs: https://www.linkedin.com/company/youth-tech-labs
• 📩 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]

💬 Know a teacher, parent, or student trying to figure out what healthy AI use should look like at school? Share this episode with them.

What happens when schools focus so hard on detecting AI that students start reshaping their own writing just to avoid suspicion?

In part 1 of 4 of our student special on AI and education, Daniel speaks with Aasha, a Grade 12 student from Waterloo, founder of Youth Tech Labs, and a young leader already helping other students think more clearly about AI, privacy, and what meaningful learning should look like now.

Aasha’s argument is sharp: what should education look like when AI is already here? She describes how AI detection tools created an environment of fear, how students were pushed to prove their innocence, and how some even began weakening their own writing just to avoid being flagged.

She also points to a gap that feels bigger than policy. Students are already using AI to study, generate practice, break down hard concepts, and explore ideas. But, schools are still treating AI literacy, privacy, and responsible use as side issues, even though these are quickly becoming part of the real world students are heading into.

Stay tuned for part 2 of the 4-part series!

🔑 What You’ll Learn in This Episode

• ✅ Why schools should focus on teaching students how to think better with AI
• ✅ How AI detection tools changed some classrooms from places of trust to places of suspicion
• ✅ Why privacy and AI literacy belong much closer to the center of this conversation
• ✅ How students are already using AI to study, test ideas, and learn at their own pace
• ✅ Why school and real-world AI use are still living in two separate worlds

🔗 Resources & Links

• 🌐 Explore Youth Tech Labs: https://www.linkedin.com/company/youth-tech-labs
• 🤝 Connect with Aasha on LinkedIn: https://ca.linkedin.com/in/aasha-khan-3a2294250
• 🌍 Learn more about Girl Up Teen Advisors: https://girlup.org/programs/teen-advisors
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💬 Know an educator, parent, or school leader trying to move from AI fear to AI literacy? Share this episode with them.

Schools are trying to figure out AI in real time, but students are already living with the results.

In this special repost episode, Daniel brings back a short conversation with Pat Belliveau to open our student series on AI and education. Pat raises a hard question: if AI is already part of the world students are growing up into, what does it mean for schools to treat it mainly as a threat? There is a real risk when teachers rely on AI detection tools that aren't reliable, and real damage that can follow when students are accused on that basis.

That is why we wanted to start the series here.

In the next few episodes, Daniel will be sharing short interviews with high school students about how they are now, really using AI, what they think about it, and how they see it shaping their future. We hear plenty from adults on this topic. This series is an attempt to make room for students to speak for themselves.

🔗 Resources & Links

• 📩 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]

💬 Know a teacher, parent, or student trying to make sense of AI in school? Share this episode with them!

What would you do if you had to start an AI consultancy from scratch today?

In this bonus clip, Daniel asks Pat Belliveau of Gambit Co exactly that. And Pat does what he does so well: he shares practical advice without posturing:

Start with someone in your network. Solve one real problem. Do it well enough to earn a case study. Then, build from there.

Thanks, Pat, for being refreshingly candid, quick to teach, and generous with lessons that many people would keep to themselves. It's why you're one of our favorite people!

🎧 Want the full conversation?

This clip comes from a longer episode on shipping AI in the real world, why so many projects fail, and what it takes to make something businesses can actually trust.

Full episode here: https://rss.com/podcasts/manaryhaus/2607646/

🔗 Resources & Links

• 🌐 Learn more about Gambit Co: https://gambitco.io/
• 🤝 Connect with Patrick Belliveau on LinkedIn: https://ca.linkedin.com/in/patrick-belliveau
• 🎗️ Explore AskEllyn: https://askellyn.ai/
• 📩 Subscribe to the Artificial Insights newsletter: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💬 Know someone trying to build an AI consultancy? Share this clip with them.

AI feels easy right up until a team tries to ship it. Patrick Belliveau of Gambit Co joins Daniel to talk about the messy middle between a promising prototype and something a business can actually trust.

In this candid conversation, Daniel and Pat reflect on what changed between year one and year two of building an applied AI company. Pat explains why Gambit moved from fixed-price projects to retainer-based partnerships, how rapid prototyping helps teams stay close to the real problem, and why so many AI projects fail before they ever have a chance to deliver.

Their conversation also explores agent orchestration, human-in-the-loop validation, the limits of black-box tools, and the organizational fear that can quietly sabotage adoption. One of the clearest ideas in the episode is that getting AI to do something once is not the hard part. Getting it to work twice, three times, and at scale is where the real work begins.

For leaders tired of vague AI promises, this episode offers a grounded look at what it takes to make AI work in the real world.

🔑 What You’ll Learn in This Episode

• ✅ Why many AI projects fail before the technology is even the main issue
• ✅ How rapid prototypes surface better feedback than long requirements documents
• ✅ Why repeatability, validation, and human-in-the-loop design matter in production
• ✅ How AI can improve both supply constraints and demand generation inside a business
• ✅ Why internal communication can determine whether adoption succeeds or stalls

🔗 Resources & Links

• 🌐 Learn more about GambitCo: https://gambitco.io/
• 🤝 Connect with Patrick Belliveau on LinkedIn: https://ca.linkedin.com/in/patrick-belliveau
• 🎗️ Explore AskEllyn: https://askellyn.ai/
• 📩 Subscribe to the Artificial Insights newsletter: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💬 Know someone trying to move AI from prototype to production? Share this episode with them.

AI can automate matching and readiness checks in lending. It cannot automate trust.

In this bonus clip, Daniel asks Sharmeen Aqeel how Lyyvora will scale as borrower volume grows. Sharmeen’s answer is simple: at an early-stage fintech, one broken interaction can damage credibility. Even if AI produces the "right" output, a human still needs to verify, interpret context, and provide real connection when borrowers are anxious or unsure.

Sharmeen also looks ahead. If a "borrower-to-offers" workflow becomes trivial in a few years, Lyyvora's moat is not the application flow. It's the network and community she's building, and the trust that comes with it.

🎧 Want the full conversation?

This clip comes from a longer episode on human-centered design in lending, AI as a founder multiplier, and why accessibility, not obstruction, is the real gap in healthcare finance.

Full episode: https://rss.com/podcasts/manaryhaus/2565271/

🔗 Resources & Links

• 🌐 Lyyvora: https://lyyvora.com/
• 🤝 Connect with Sharmeen on LinkedIn: https://www.linkedin.com/in/sharmeen-aqeel/
• 📩 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]

💬 Know a founder building an AI-enabled marketplace? Share this clip with them.

Clinics get stuck in lending for a frustratingly simple reason: the process is hard to navigate. The information exists, lenders are willing, and qualified borrowers do get funded. But the path is not accessible, especially when you're running a clinic and don't have time to decode criteria buried across pages, videos, and jargon.

Sharmeen Aqeel is the founder and CEO of Lyyvora, and she treated this as a human-centered design problem. Lyyvora is a Lending-as-a-Service platform built for healthcare and medical aesthetics clinics, designed to make “what happens next” clear: one streamlined intake, prescreening for readiness, and matching to vetted lenders who actually want qualified deals.

AI matters here because it lowers the cost of judgment. It helps Lyyvora turn scattered lender requirements into usable decisioning, score borrower readiness, and match clinics to the best-fit lenders. In the episode, Daniel and Sharmeen also dig into where automation stops: trust still needs a human, especially in early-stage fintech.

🔑 What You’ll Learn in This Episode

• ✅ Why “lenders want to lend” can be true while the process still feels impossible for clinics
• ✅ How human-centered design makes lending workflows legible without changing the underlying rules
• ✅ Where AI helps with readiness, matching, and speed, and where humans stay in the loop for trust
• ✅ What a lending marketplace changes for transparency and competition among lenders
• ✅ Why building the lender side of a two-sided network can be easier than reaching borrowers
• ✅ How a solo founder can accomplish what used to require a team with the help of AI
• ✅ How some things you do as a solo founder should never be delegated to AI

🔗 Resources & Links

• 🤝 Connect with Sharmeen on LinkedIn: https://www.linkedin.com/in/sharmeen-aqeel/
• 🌐 Lyyvora: https://lyyvora.com
• 🏢 Lyyvora on LinkedIn: https://ca.linkedin.com/company/lyyvora
• 📩 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]

💬 Know someone working on AI in financial services or marketplaces? Share this episode with them.

AI licensing can sound like a moral argument until you look at the product constraints. If the best material is behind paywalls and contracts, “just scrape it” stops working.

In this bonus clip, Daniel pressures the obvious skeptical question: if big AI companies can afford lawsuits, why bother building fair, legal access at all? Julie Trelstad’s answer is practical. She expects the next wave of AI advancements to include many more small, domain-specific models, and those models will need verified, high-quality sources like textbooks and peer-reviewed journals.

🎧 Want the bigger picture?

This clip is one piece of a longer conversation with Julie about “AI rights” in publishing, how provenance and permissions can become machine-readable, and what it could look like for creators to get paid when their work is used.

Check out the full episode here: https://rss.com/podcasts/manaryhaus/2525377/

🔗 Resources & Links

• 🌐 Julie’s work at Paperbacks & Pixels: https://paperbacksandpixels.com/
• 🧩 Amlet (register and license AI rights): https://amlet.ai/
• 📩 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]

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