Incongruent

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What if AI literacy wasn’t about prompts or platforms, but about thinking that endures? We sat down with AI strategist and educator Yara Alatrach to unpack how schools can move past the hype and build true fluency—skills that help students question systems, evaluate data, spot bias, and decide when human judgment belongs in the loop.

Yara’s journey from reservoir engineering to consulting and AI leadership at Microsoft gives her a rare vantage point: end users, tech teams, and leaders speak different languages. She explains how that gap shows up in classrooms and why a cross-curricular, teacher-friendly approach matters. We get into the UAE’s bold AI mandate, the idea of treating technology as nation building, and what it looks like to design a weekly, one-hour curriculum that fits real schedules while raising the bar on critical thinking.

We also tackle the hard line between education and product training. Yara makes the case for vendor-agnostic AI education that still welcomes public–private collaboration, so students learn concepts they can transfer across platforms—whether that’s Azure, Google, or open-source stacks. From embedding ethics in every lesson to preparing 10-year-olds for a 2035 workforce, this conversation maps the practical path from curiosity to confidence.

If you care about edtech that empowers teachers, AI literacy that sticks, and a future-ready generation that can frame problems and ask better questions, this one’s for you. Follow the show, share with a colleague, and leave a review to tell us how your school is approaching AI—and what you want us to explore next.

Links:

https://www.ednas.academy/

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What if you could stress‑test a policy pitch with a faithful simulation of the minister you’re meeting tomorrow? We sit down with Leon Emirali—startup founder turned Westminster aide—who’s now building Nostrada AI, a platform that creates high‑fidelity digital twins of politicians trained only on their own public words. From late‑night Brexit votes to the first days of COVID, Leon’s time inside government shapes a clear-eyed view of how information moves, where decisions bottleneck, and why faster, more grounded research can improve the conversation between industry and state.

We dig into the mechanics: how per‑politician models reduce cross‑bias, why provenance and citations matter, and where to draw bright lines between public records and private data. We also probe the big question lighting up headlines—AI “ministers” and democratic legitimacy. Lebanon and Albania are experimenting; should the UK or US follow? Leon argues for AI as a tool, not a ruler, and lays out practical guardrails that keep expertise accessible while preventing false authority and misuse.

Beyond politics, we explore open data as a strategic asset for smarter public services—think anonymized NHS patterns, transport peaks, and service planning informed by real signals. And for builders, Leon shares hard-won advice: say yes, ship sooner than feels safe, and let feedback compound. If you care about policy, lobbying, AI ethics, or the future of democratic engagement, this conversation offers concrete insights, skeptical optimism, and a working blueprint for responsible adoption.

If this sparked ideas, follow the show, share with a friend who loves tech and policy, and leave a quick review so more curious people can find us.

Links:

https://www.leonemirali.com/

https://www.nostrada.ai/

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Tired of saving dream trips you never book? We sat down with Seeq founder John Levesque to unpack a new path from “that looks amazing” to “I’m going”—and why creators, not platforms, should capture most of the value along the way. John spent years building communities for brands at Microsoft and DocuSign, saw creators’ work powering growth without fair returns, and decided to flip the economics. Seeq’s AI ingests social travel videos, parses both visuals and narration, and turns them into mobile-friendly, SEO-optimized guides with maps and bookable links. The kicker: creators earn 60% of the revenue from bookings sparked by their guides.

We dig into the problem behind the problem: discovery that ends in an overstuffed save folder, endless lists that don’t match your taste, and the broken handoff between inspiration and planning. John explains how Seeq shortens that gap, and how ethical data use can be a competitive edge. We also explore the future of personalization—preference signals that feel like a compass, not a prison—and how wearables could add context to “relaxing” or “thrilling” without hijacking your attention. Instead of replacing human judgment, Seeq treats creators as the new search engine for experiences, rewarding specificity over generic top 10s.

If you’ve ever spent 30 hours planning a 7-day trip, this conversation will feel like a breath of fresh air. Expect real talk on AI ethics, the market for synthetic influencers, and a model that lets creators double or triple dip—brand deals, social views, and now bookings—without selling out their voice. Subscribe, share with a traveler who’s drowning in tabs, and leave a review to help more people find the show.

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Monica Marquez, workplace AI strategist and founder of Flipwork Inc., shares insights on how organizations can adapt to AI-driven changes in the workplace through inclusive practices and human-centered transformation. She explains why companies struggle with AI adoption despite significant investments, pointing to outdated training methods and the need for alignment between identity, mindset, and environment.

• AI is changing jobs faster than people can change how they work
• Bias-free AI is unrealistic – the goal should be making bias visible and accountable
• Diverse perspectives in AI training data lead to more inclusive systems
• Cultural norms and beliefs can create barriers to AI adoption, especially for marginalized groups
• Management needs to create cultures where AI experimentation is encouraged
• Leaders should identify workflows where AI can provide significant leverage
• The goal isn't to do the same things faster but to reimagine processes entirely
• Adaptability is now the "golden skill" in an era of constant workplace change
• "If you don't use AI, someone who leverages AI will replace you"

Don't wait - start using AI today, even in small ways. Ask better questions about what data is being used and what assumptions are baked into the outputs. Focus on workflows - reimagine your work so AI can help you achieve more, not just do the same things faster. Check out our newsletter "The Flip" at gettheflip.com for weekly insights on AI adoption and workplace transformation.

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Dan Sickles, award-winning filmmaker and founder of Depop Studios, shares his insights on incorporating AI into filmmaking while preserving artistic integrity. He discusses his approach to crafting narratives that center underrepresented communities and his vision for multidimensional storytelling through his new project "New Here."

• Dan is drawn to communities living on society's fringes where new rules and identities emerge
• AI serves as a valuable tool in the pre-visualization and ideation phases of filmmaking
• "You can't prompt taste" - human creativity and judgment remain essential despite AI advancements
• AI helps extend limited budgets, allowing independent filmmakers to hire more people
• The "New Here" project aims to be an infinitely extendable film centered around community and co-creation
• Future storytelling will blur boundaries between media: "films are games, are series, are posters, are memes"
• World-building will become increasingly important as creators gain more tools to curate digital experiences

Stay up to date with Dan's projects at depopstudios.com and through their Substack newsletter.

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Chris Daigle, founder and CEO of Chief AI Officer, helps executive teams deploy real-world AI strategies that drive business results through his flagship Executive AI Immersion program, designed specifically for mid-market companies.

• AI adoption is fundamentally different from previous tech waves because it's accessible to anyone regardless of technical background
• Older generations may actually have an advantage in using AI due to their business experience and ability to provide better context
• Companies that delay AI adoption create an exponential disadvantage as early adopters gain market share and embed AI in their culture
• The Stanford/Harvard/BCG study found AI-enabled workers produced 12% more, 25% faster, and with 40% higher quality
• The traditional "good, fast, cheap - pick two" paradigm no longer applies with AI-enhanced knowledge work
• Leadership teams should get the same AI training to ensure alignment before implementation
• AI governance should include clear use policies with employee training and acknowledgment
• IT departments should handle analytical AI (data science) while operational teams implement generative AI in business processes
• AI implementation is "boring" - it's about systematically improving thousands of small business processes
• Executives should develop the reflex of asking "Can I use AI for this?" for every business challenge

Start using AI today by simply asking it how it can help with your specific role or challenge - the more you practice, the more effective you'll become.

Chris Daigle:

https://www.linkedin.com/in/doctordaigle/

Links: https://www.chiefaiofficer.com/

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Alistair Lowe Norris, Chief Responsible AI Officer at Iridis, discusses how AI can be a positive force for change when built on ethical foundations, safety measures, and benefits for both people and planet.

• Recipient of the President's Lifetime Achievement Award for his volunteer work addressing food insecurity
• Three pillars of responsible AI: ethical AI that follows guidelines, safe AI that minimizes risks, and beneficial AI for people and planet
• Growing complexity of AI regulation with hundreds of non-harmonized regulations worldwide
• Transition from humans operating systems to supervising autonomous AI agents
• Corporate regulation of AI through procurement standards (Netflix, Microsoft examples)
• Critical need for AI governance frameworks within organizations
• Change management principles for navigating technological transformation
• Career opportunities in responsible AI as companies seek to navigate complex regulatory landscapes
• Importance of AI literacy and critical thinking skills when using AI tools

If you're interested in learning more about responsible AI, there are many open classes and resources available. This is the perfect time to get involved as AI governance becomes increasingly crucial in the coming years.

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Radhika Dutt challenges traditional goal-setting frameworks and introduces OHLs (Objectives, Hypotheses, and Learnings) as a more effective alternative for today's knowledge workers who are solving complex puzzles rather than performing repetitive tasks.

• Goals and targets originated in the 1940s for unskilled, unionized workers doing repetitive tasks
• Today's workforce needs a puzzle-solving mindset rather than target-hitting approach
• AI accelerates "enshittification" by optimizing metrics while hiding negative consequences
• OHLs framework asks: How well is it working? What have we learned? What are we doing next?
• Even middle managers can start implementing OHLs by reframing their own work as puzzle-solving
• Successful organizations create cultures of psychological safety where honest discussions about successes and failures can occur
• Intel's success came from Andy Grove's "paranoid obsession with never getting complacent," not just OKRs
• The OHLs template is available at radicalproduct.com

Radhika invites listeners to reach out to her on LinkedIn and share their experiences implementing OHLs, as she's currently writing her second book and looking for case studies.

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Galvin Widjaja, founder and CEO of Lauretta AI, shares his journey from restaurant management to leading a privacy-first computer vision company that predicts human behavior without using biometrics. His unconventional path provides unique insights into building AI that respects privacy while effectively understanding human intent.

Here are the seven biggest takeouts from the interview:

1. AI Should Predict Human Intent — Without Biometrics

2. Security vs Retail: Same Tech, Different Purpose

3. From Fried Noodles to Frontier Tech

Galvin’s entrepreneurial roots run deep — from his father’s fried noodle shop in Jakarta where he managed three migrant-serving restaurants in Singapore. That hands-on experience with human behavior in physical spaces now powers his AI vision.

4. The YouTube Problem in AI Training

5. Privacy by Design, Not by Patch

6. Winning Homeland Security Without a US Office

Despite being headquartered in Singapore at the time, Lauretta AI won its first Homeland Security contract in 2019 — a signal that vision and trust can matter more than geography.

7. AI’s Evolution: From Detection to Understanding

Galvin sees AI as progressing in stages: inference → classification → aggregation → higher-order thinking. Lauretta’s bet is on this next frontier: AI that doesn’t just classify behavior, but understands context.

If you're appreciating all topics on AI and these interviews, please subscribe, like, and comment where possible. We really appreciate your support!

Links:
https://lauretta.io/

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The biggest challenges in AI implementation aren't the headline-grabbing risks but the hidden ones stemming from human factors and organizational dynamics. Drawing parallels to aviation safety, successful AI deployment requires understanding how people interact with these tools in complex organizational settings.

• Hidden AI risks often come from human-system interactions rather than technical failures
• Real examples show how AI can create burnout, enable misinformation spread, or amplify existing biases
• Standard AI safety approaches often miss these subtle but critical issues
• The "Adopt, Sustain, Optimize" framework helps track the user journey through AI implementation
• Six categories of hidden risks include quality assurance, task-tool mismatch, and workflow challenges
• Proactive "pre-mortem" approaches are more effective than waiting for problems to emerge
• Human oversight only works when people have expertise, time, and authority to challenge AI outputs
• Successful implementation requires diverse teams, tailored training, and leadership understanding
• Measuring impact should go beyond efficiency to capture quality improvements and risk management
• Building resilient sociotechnical systems means designing for human realities, not just deploying technology