“The best AI users are always women, and they’re always super high creative types.”

Jonathan Green has seen what many AI builders overlook: the most effective AI users aren’t technical experts: they’re creative communicators who treat AI like a teammate. They name their agents, talk to them conversationally, and rarely get frustrated when something doesn’t work.

After being fired from his university job in a snowstorm, Jonathan built a business around real-world AI use. From a tropical island, he now consults on AI automation and business workflows, helping teams solve problems without overengineering.

In this episode, Daniel and Jonathan talk about why most companies don’t need more AI, what customer support teams reveal that bots can’t, and how AI agents with personalities, skill trees, and logic chains are more effective.

🔑 What You’ll Learn in This Episode

• ✅ Why the best AI users may not look like who you expect
• ✅ How personality and skill trees make agents more effective
• ✅ Why customer support teams should never be replaced outright by AI
• ✅ How “friendship-style prompting” outperforms command-and-control approaches
• ✅ What most companies really need when they ask for AI

Resources & Links

• 🔗 Connect with Jonathan on LinkedIn: https://www.linkedin.com/in/servenomaster/
• 📖 Learn more about Jonathan: https://servenomaster.com/
• 📩 Subscribe to the Artificial Insights newsletter for summaries and takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

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

Investors often ask Carlos Almeida the same question: if your AI is built on someone else’s model, what’s your moat?

For him, the answer starts with building what the model can’t: controls that keep AI aligned with a company’s goals, interfaces that let humans guide its decisions, and ways to manage and expand its usable data.

In this conversation, Daniel and Carlos explore the risks of relying on a model’s default behavior, the challenge of separating fact from fabrication, and why adding trust and control might be just as valuable as improving accuracy.

They dig into the less visible work required to turn a general AI into something a business can rely on.

🎧 Want the bigger picture?

This clip is just a glimpse of Carlos’s perspective. In the full episode, he shares how copilots can outperform chatbots, the traps companies face when building their own support AI, and why automation might expand demand for customer support instead of replacing it.

Listen to the full episode: https://rss.com/podcasts/manaryhaus/2082407/

🔗 Resources & Links

• 🤝 Connect with Carlos: https://www.linkedin.com/in/carlos-alm/
• 🤖 Learn more about Optave: https://optave.com/
• 📩 Subscribe to the Artificial Insights newsletter for summaries and takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

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

AI customer support can improve service without replacing people, and Carlos Almeida believes that starting with automation is the wrong approach.

At Optave, the company he co-founded, the focus is on AI copilots that help enterprise customer support agents work more effectively, with automation only when the conditions are right. That means mapping every step of a support journey, building AI quality controls so responses meet standards, and giving companies the ability to guide what the AI is doing as part of a broader enterprise automation strategy.

Carlos has worked in customer support long enough to see the gap between marketing promises and what AI can actually deliver: from overhyped automation to quick prototypes that create more problems than they solve.

In this episode, he talks with Daniel about the traps companies face when building their own customer experience AI, why control and trust are as important as accuracy, and how automation might expand demand for customer support instead of replacing it.

🔑 What You’ll Learn in This Episode

• ✅ Why AI copilots can be a better first step than full automation
• ✅ How to build AI quality controls that keep responses on track
• ✅ Why companies overestimate what AI can do in support
• ✅ The hidden costs of quick AI prototypes
• ✅ How automation could expand demand for customer support rather than replace it

🔗 Resources & Links

• 🤝 Connect with Carlos: https://www.linkedin.com/in/carlos-alm/
• 🤖 Learn more about Optave: https://optave.com/
• 📩 Subscribe to the newsletter: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

🚀 Enjoyed this conversation? Pass it along to someone building AI systems their customers can trust.

AI hype cycles come and go, but the difference with this AI wave is how widely it touches every corner of business and work.

Mike Kirkup has seen big shifts before, from the rise of smartphones at BlackBerry to the explosion of web apps, but he says AI feels different because “everybody’s working on it,” even if not everyone realises it yet. That ubiquity creates both urgency and overwhelm: whole functions like customer success may need to be rethought, while companies like Klarna are already reversing course on automation-first strategies.

In this bonus clip, Daniel and Mike talk about why AI’s spread feels like a social experiment playing out in real time, what makes this moment unlike mobile or Web 2.0, and why sometimes the best sales script might start with: “Hi, I’m a human.”

🎧 Curious for more?

This clip is just a glimpse of Mike’s perspective. In the full episode, he shares how Arlo spun out of EnPowered, what it takes to build AI into lending workflows, and why domain expertise is as important as the AI itself.

Listen to the full episode: https://rss.com/podcasts/manaryhaus/2061508/

🔗 Resources & Links

• 🤝 Connect with Mike on LinkedIn: https://www.linkedin.com/in/mikekirkup/
• ✨ Learn more about Arlo: https://usearlo.com
• 📩 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]

💬 Found this clip useful? Share it with someone rethinking where AI fits in their business.

AI is changing the way lenders and brokers process deals, and it’s not by replacing people, but by rethinking how the work gets done.

Mike Kirkup didn’t set out to build a product for the finance industry. As CTO at EnPowered, he spent too much time on the repetitive work of gathering and verifying loan documents. Solving that problem led to building what would become Arlo, an AI-powered loan processing platform for commercial lending and document automation.

Today, Arlo is used by lenders and brokers across North America to streamline loan document collection and verification. Instead of asking borrowers to upload documents one at a time, it lets them “just dump all of the documents.” The AI then identifies, extracts, and verifies what’s needed, improving speed and accuracy in loan processing workflows.

In this episode, Daniel and Mike discuss designing AI in finance with domain expertise, what happens when AI starts asking its own questions, and how building for AI from the start changes the borrower experience. They also explore the balance between giving AI more room to “play” and making sure it doesn’t ask a “stupid question” in front of a customer.

🔑 What You’ll Learn in This Episode

• ✅ How Arlo started as an internal tool and why partners asked to use it
• ✅ How AI changes the borrower experience in commercial loan processing
• ✅ Why domain expertise matters as much as the AI itself
• ✅ How to design for AI from day one instead of bolting it on later
• ✅ The trade-offs between control, unpredictability, and trust in AI

🔗 Resources & Links

• 💼 Connect with Mike on LinkedIn: https://www.linkedin.com/in/mikekirkup
• 🏦 Learn more about Arlo: https://usearlo.com
• 📩 Subscribe to the Artificial Insights newsletter for summaries and takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

⭐ Found this useful? Leave a review and share it with someone building in AI!

Electricity didn’t just replace coal, it also created television, film, and entire industries no one predicted. What surprising roles might AI play in our lives?

In this bonus episode, Daniel and Chyngyz Dzhumanazarov explore the future of human–AI connection. They talk about AI as coach, companion, and collaborator, and even share a provocative hot take: in the next decade, human–AI marriages might not seem so impossible.

They also reflect on history’s surprises, from electricity leading to television to new industries no one predicted, and ask what unexpected opportunities AI might open next.

🎧 Curious for more?

This clip is part of Daniel’s longer conversation with Chyngyz, founder and CEO of Kodif. In the full episode, he shares how Kodif is rethinking the “atomic unit of work” in customer support, why naivete can be an advantage in AI, and how new roles like “AI Manager” are emerging.

Listen to the full episode: https://rss.com/podcasts/manaryhaus/2041601/

🔗 Resources & Links

• 🤝 Connect with Chyngyz on LinkedIn: https://www.linkedin.com/in/cdzhuman/
• ✨ Learn more about Kodif: https://kodif.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]

💭 Found this perspective fresh? Share it with someone curious about how AI is reshaping human connection.

Customer support has always been built around humans doing the work. At Kodif, Co-Founder and CEO Chyngyz Dzhumanazarov is rethinking that “atomic unit of work” with AI customer support agents that can resolve complex tickets, spot patterns in incoming cases, and recommend process improvements, all while keeping human oversight in place.

Kodif’s AI automation platform empowers non-technical teams to design, test, and run their own support workflows. The result: faster resolutions, better insights, and new roles like “AI Manager” appearing inside customer experience teams.

In this episode, Daniel talks with Chyngyz about why the first wave of AI support tools often failed, how Kodif is changing the way companies approach CX automation, and why naivete can be an advantage when building in AI. They also explore how trust, transparency, and human coaching keep AI from becoming a black box.

🔑 What You’ll Learn in This Episode

• ✅ Why the “atomic unit of work” in support is shifting from humans to AI
• ✅ How Kodif empowers non-technical teams to build and iterate on AI automations
• ✅ Why naivete can be a strength for a non-technical founder in AI
• ✅ How trust and transparency shape AI adoption in customer experience
• ✅ Why job titles like “AI Manager” are starting to appear in support teams

🔗 Resources & Links

• 🤝 Connect with Chyngyz on LinkedIn: https://www.linkedin.com/in/cdzhuman/
• 🤖 Learn more about Kodif: https://kodif.ai/
• 📩 Subscribe to the Artificial Insights newsletter for summaries and takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

🚀 Enjoyed this episode? Leave a review and share it with someone building in AI or ops!

For builders, community is more than just an audience, it’s the foundation of the product. After all, those early users shape the product.

For Tair Asim, Sage worked at buildspace because the community already trusted the brand. That trust meant people filled out profiles, gave feedback, and kept showing up, even when the product was rough around the edges.

In this bonus episode, Daniel and Tair explore the connection between brand and product, why community buy-in matters for marketplace-style tools, and how focusing on a specific niche can make growth faster and more meaningful.

🎧 Want to hear more?

In the full episode, Tair shares his journey from DevOps to product leadership, why vertical integration can protect against competitors, and how Sync is training its own models to make video editing in the browser simple and powerful.

Listen to the full episode: https://rss.com/podcasts/manaryhaus/2024397/

🔗 Resources & Links

• 🐦 Connect with Tair on X: https://x.com/tair
• ✨ Learn more about Sync: https://sync.so/
• 📩 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]

💡 Heard something useful? Share this clip with a teammate who’s building around community.

AI isn’t just changing how we work. It’s also reshaping how we meet the people we work with.

For Tair Asim, that started with Sage, where he learned AI matchmaking isn’t just for dating apps, it can connect builders, spark new companies, and even launch unexpected collaborations. Instead of filling out forms, Sage asked questions in natural conversation, learned what people cared about, and connected them with others who shared their goals. The result was unexpected collaborations: from a footballer in Milan launching an AI project with a neighbor he’d never met, to community members finding co-founders halfway across the world.

Now, as Head of Product at Sync, Tair is applying the same focus on inputs, user experience, and vertical integration to AI video editing. Sync trains its own models for lip syncing and video modification, aiming to make editing in the browser as simple as writing an email.

In this episode, Daniel and Tair talk about why brand and community can make a product work where others fail, the shift in user interfaces as AI becomes part of everyday tools, and why owning a vertical from end to end can be a long-term advantage.

🔑 What You’ll Learn in This Episode

• ✅ How natural language onboarding can improve AI-driven matching
• ✅ Why high-quality inputs are essential for reliable AI outputs
• ✅ How community and brand shape early adoption
• ✅ The challenges of training AI for video vs. text
• ✅ Why vertical integration can protect against fast-moving competitors

🔗 Resources & Links

• 👨‍💻 Connect with Tair on X: https://x.com/tair
• 🎥 Check out Sync’s latest product launches: https://sync.so/
• 📩 Subscribe to the Artificial Insights newsletter for episode recaps and key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

🚀 Inspired? Share this episode with someone working on AI matchmaking, vertical integration, or building with community in mind!

Startups rarely fail because the product doesn’t work. More often, they fail because no one knows the product exists.

Alex Millar, co-founder and CTO of GovAI, has seen how technical founders can undervalue sales and marketing. Building something that resonates is only half the challenge. Getting it into people’s hands, and knowing when to invest in sales talent, is what keeps companies alive.

In this bonus clip, Alex talks about why “if a tree falls in the forest” is a dangerous mindset for founders, why timing matters when bringing on sales and marketing, and why relying on a product to go viral is rarely a strategy.

🎧 Want the full story?

In the full episode, Alex and Daniel dive deeper into selling AI to government, why pricing models can be as important as features, and how retrieval-augmented generation (RAG) and evaluation testing shape the next generation of AI products.

Listen to the full episode: https://rss.com/podcasts/manaryhaus/1962723/

🔗 Resources & Links

• 🤝 Connect with Alex on LinkedIn: https://www.linkedin.com/in/alxndrmlr/
• 🌐 Learn more about GovAI: https://govai.com/
• 📩 Subscribe to the Artificial Insights newsletter for summaries and takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💡 Found this clip useful? Share it with a founder who’s learning when to lean into sales.