Founder's Story

Daniel and Kat explore the origin story behind Ritual, starting with Kat being four months pregnant and unable to find a prenatal vitamin she trusted. Kat explains how women’s health has been underfunded, understudied, and underestimated, and how that gap became the foundation for a brand built on transparency, science, and trust. The conversation covers venture funding, building while raising three children, the power of starting narrow, the problem with copycat supplements, and why Ritual has invested millions into human clinical studies instead of relying on marketing claims.

Key Discussion Points

Kat shares that Ritual began when she was pregnant and saw a major gap in women’s health, especially around prenatal nutrition, postpartum, perimenopause, and menopause.

She recalls an investor telling her she could either start a family or start a business, but not both, and explains how that moment became fuel to build Ritual into a nine-figure company.

Kat explains why she rejects the idea that women’s health categories are “niche,” arguing that these experiences make up the collective women’s health journey.

She breaks down Ritual’s strategy of starting with one product, one price point, and one clear promise, instead of launching everything at once and standing for nothing.

Kat shares how customer demand led to major product expansion, including a gut health product that did $30 million in first-year sales.

She also explains when Ritual does not simply follow customer requests, using choline as an example of a science-led product customers did not yet know they needed.

The conversation goes deep into women’s health research, including postnatal nutrition, breast milk quality, methylated folate, and the problem of studies being run on men or animals while marketed to women.

Kat explains why Ritual raised venture capital from the beginning: to build from the ground up, invest in scientists, clinical validation, sourcing, delivery technology, and patented formulations.

She opens up about sacrifice, saying she tries to throw guilt out the window and focus on three priorities: health, business, and family.

Kat reflects on success, saying she often struggles to feel like she has “arrived” because every milestone leads to the next one.

Takeaways

Women’s health is not niche, it has simply been underfunded, understudied, and underestimated for too long.

Starting narrow can build more trust than launching wide, because focus helps a brand stand for something clearly.

In supplements, marketing is not enough; clinical studies, sourcing, safety, and delivery technology are what separate real innovation from private-label copycats.

Motherhood and entrepreneurship are not separate lanes for Kat, they are intertwined, and being the customer helped her build for the customer.

For founders, guilt can become a trap, so Kat’s framework is choosing the few things that matter most in a given season and accepting the tradeoffs.

Closing Thoughts

Kat Schneider’s story is about more than building Ritual. It is about proving that overlooked markets are often massive opportunities hiding in plain sight. This episode shows how trust becomes a moat when a founder is willing to build slower, go deeper into science, and solve a problem she understands personally. For anyone building in health, CPG, or women’s wellness, Kat’s message is clear: don’t copy what already exists, build what people actually need.

 

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Daniel and Cylton Collymore dive into one of the biggest questions facing founders right now: what happens when AI becomes essential, but the cost and control of that AI sits in someone else’s cloud. Cylton explains why he believes LLMs are still “book smart, not street smart,” why giving AI agents full access to your computer is like handing your bank account to someone on a second date, and why the future may shift toward local or on-prem AI systems. The conversation also turns personal as Cylton shares how being laid off from Meta after his 50th birthday forced him into Plan A, why Sirsi is his contribution, and why access—not talent—is the real bottleneck holding back the next generation of builders.

Key Discussion Points

Cylton pushes back on the AGI hype, arguing that LLMs present well but are still limited, comparing them to someone who can pass every quiz but struggles with real-world “street smart” reasoning.

He warns founders about giving AI agents full access to their computers, phones, finances, and private data, comparing it to giving a new relationship access to your bank account on the second date.

Cylton explains that agentic systems should support human intelligence, not replace it, especially in roles like cybersecurity where human judgment is needed as the “tiebreaker.”

He shares the deeper vision behind Sirsi: giving people and companies AI efficiency without requiring them to become AI experts or build an AI company from scratch.

The conversation explores why talent is everywhere, from government to Meta to Howard University, and why the future depends on expanding access, encouragement, tools, and visibility for more builders.

Cylton opens up about being laid off from Meta in 2023 and realizing that Plan B was not enough, saying that if you have a Plan B, you do not really have a plan.

He gives founders a blunt lesson on equity: do not give away shares casually, because equity is not candy, it is a claim on your time, effort, and future money.

Cylton explains why he is betting against the purely cloud-centric AI model, arguing that inference costs, token economics, outages, and lack of control will push more AI back to local devices and on-prem systems.

He compares token economics to Chuck E. Cheese at trillion-dollar scale, warning that today’s AI usage is subsidized by investor capital and that prices are already shifting upward.

The episode closes with Cylton pointing toward a hybrid AI future where frontier cloud models still exist, but daily business AI increasingly runs closer to the user through local hardware and private systems.

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Takeaways

AI agents are powerful, but founders should not confuse convenience with safety, especially when private data, financial systems, and company operations are involved.

The next AI advantage may not be who has the best prompt, but who controls their infrastructure, costs, data, and inference layer.

Equity is one of the most expensive things a founder can give away, because it represents a claim on years of work, not just a future payout.

Access is the real unlock: more people building with better tools could create a much larger economy and surface talent that traditional systems overlook.

The current AI land grab is being subsidized, and founders should prepare for a world where token costs, cloud dependence, and AI usage become real operating expenses.

Closing Thoughts

Cylton Collymore brings a rare mix of government, cybersecurity, big tech, and founder perspective to the AI conversation. This episode is not anti-AI, it is a warning against using AI blindly before understanding the cost, control, and security tradeoffs. His message to founders is clear: use AI, but do not outsource your company’s future to systems you do not control.

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Daniel and Kris Fishman explore why compounding pharmacies are suddenly part of the national health conversation, especially as GLP-1s, peptides, and personalized medicine become mainstream. Kris breaks down what compounding actually means, why “one size fits all” medicine is being questioned, and how pharmacies like Wells step in when traditional options are unavailable or not personalized enough. The conversation also covers FDA scrutiny, peptide regulation, Big Pharma tension, operational scale, and the emotional reality of leading a fast-growing healthcare company.

Key Discussion Points

Kris explains that compounding pharmacies go back centuries and combine active pharmaceutical ingredients to create personalized preparations when standard retail versions do not fit a patient’s needs. 

He breaks down why peptides became a major health trend, describing them as amino acid chains that have gained traction through wellness culture, social media, and gray-market demand. 

Kris discusses the FDA’s review process and the importance of the upcoming PCAC meeting, where physicians and pharmacists can explain why certain peptides should be available for compounding. 

He explains that Wells Pharmacy Network focuses on quality, testing, sterile processes, and transparency, encouraging people to research their pharmacy and understand its safety standards. 

Kris shares that the company’s operational unlock came from improving speed, technology, and order fulfillment while maintaining strict quality control. 

He talks about the tension with Big Pharma, especially after GLP-1 shortages, and argues compounding exists to help patients where personalized or unavailable options are needed. 

Kris says the biggest CEO pressure is not product quality, because he trusts his team, but making sure regulators understand the data, adverse effects, and patient impact behind the products they carry. 

He opens up about the personal cost of leadership, including missing much of his children’s lives while building the company, and the tradeoff of trying to provide a better future for them.

Takeaways

Personalized medicine is growing because more people are questioning whether one-size-fits-all healthcare actually serves their needs. 

The biggest misunderstanding about compounding is safety, which is why quality controls, testing, and regulatory transparency matter. 

Peptides are popular, but Kris warns that people should avoid gray-market options and work through qualified doctors and reputable pharmacies. 

Scaling in healthcare requires both speed and trust, because getting products out quickly means nothing if quality is compromised. 

Leadership can create real personal sacrifice, and Kris reflects honestly on the cost of building while raising a family.

Closing Thoughts

Kris Fishman’s story shows the intersection of healthcare, entrepreneurship, regulation, and personal mission. What started as an industry he did not fully understand became a calling after seeing patients and family members benefit from personalized products. This episode gives listeners a clearer picture of why compounding pharmacies matter, why peptides are everywhere, and why the future of medicine may depend on safer, smarter personalization.

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Daniel and Tal trace Tal’s origin story from writing code in elementary school to building Cloudinary with two co founders over decades of friendship. Tal explains why bootstrapping forced discipline and protected culture, how Cloudinary grew product led before “PLG” was a label, and what it means for employees when option value rises without constant dilution from new funding rounds. The conversation then pivots into the AI era, where Tal is actively experimenting with AI assisted coding systems, and where he predicts both huge opportunity and a much bigger roller coaster for founders.

Key Discussion Points

Tal shares that computers felt like a creative superpower because he could imagine something and make it real, and that drive never left him.

He explains how the act of building changed from BASIC and Pascal to orchestrating AI agents, but the core satisfaction is still creation.

Tal breaks down why bootstrapping is harder than fundraising, because you must earn revenue fast, spend with discipline, and grow at a pace that preserves culture.

He explains the upside of founder control, the board remains the founders, allowing them to design the company’s path, values, and hiring standards.

Tal describes a hidden employee benefit of bootstrapping, option value can grow with revenue without the repeated dilution and preference complexity of venture rounds.

He shares a hard learned go to market lesson, moving too far upmarket too quickly lowered win rates and created lottery quarters, forcing a return to a healthier range before expanding again.

Tal explains why the barrier to starting companies keeps dropping, first cloud enabled Cloudinary, now AI agents enable coding, marketing, and selling, making “zero person companies” plausible.

He pushes back on the idea that everything becomes a race to zero price, arguing that enterprise grade reliability, compliance, and the cost of mistakes create a durable moat.

Tal describes the co founder operating system that kept them together for 30 years, weekly conversations, honest venting, and deep understanding of when to push and when to back off.

He closes with a grounded view on entrepreneurship, it is a little crazy, rules keep changing, and not everyone should be a founder, but impact is possible as an employee too.

Takeaways

Bootstrapping is a strategy, not a flex, it buys control and cultural consistency, but demands early revenue and relentless discipline.

AI will make building cheaper and faster, but it will also make competition faster, and real moats will come from reliability, trust, and execution in complex environments.

If you go upmarket too soon, you can trap yourself in “lottery quarters,” so watch win rate as closely as deal size.

Three co founders can be a strength because it creates a balancing mechanism, especially when conflict or pressure rises.

The future may reduce scarcity, but humans will still need purpose, meaning, and a way to feel impactful, even if robots do more of the work.

Closing Thoughts

Tal Lev-Ami is a rare blend of builder and long game operator, someone who scaled without losing the craft and then returned to hands on creation through AI. This episode is a reminder that the best companies endure because they earn trust, not because they raise the biggest round. In an era where anyone can ship fast, Tal’s message lands hard: the real edge is building something that keeps working when the stakes are high.

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Daniel and Harrison Allen Lewis break down why the best leadership lessons often come from terrible management, and why clarity beats charisma in modern organizations. Harrison explains his operating model for transformation: define the outcome, anchor a strategy to that outcome, then build a plan that the business can own. They also explore career leverage, mentorship, fear as a signal, and why great CIO work is less about tools and more about aligning people, incentives, and accountability.

Key Discussion Points

Harrison shares the grocery store story that stuck with him: he thought he was trusted with the keys, then learned later it was an escape during a bomb threat, which taught him accountability the hard way.

Daniel and Harrison discuss why many companies promote or place people without matching strengths, and why the better approach is doing change with people, not to people.

Harrison explains the transformation chain: outcome first, then strategy, then plan, and why starting with a plan creates chaos.

They unpack differentiation in the AI era, where tools are everywhere, and the edge is understanding value, willingness to pay, and the unique properties of your tools.

Harrison calls out silos as a major failure point, where people show up as their function instead of as business owners solving a shared problem.

He shares his worst CIO moments: being asked to execute a doomed plan, or being the new leader who tells the uncomfortable truth and becomes the most hated person for a month.

Harrison describes the loneliness of leadership and how he leaned on reciprocal mentors and peers as a sounding board.

He argues EQ will matter more, but AI can increase effective IQ by offloading minutiae into a knowledge base so leaders can operate at a higher level.

Harrison closes with his butterfly effect story: a Kroger manager handing him an application changed everything, and his father’s rule became his compass: say yes unless you have a good reason to say no, and fear is not a reason.

Takeaways

The best leaders are transparent and candid, and they treat people as capable adults who can handle the truth.

If your plan is not anchored in strategy and your strategy is not anchored in outcome, your transformation is already failing.

Silos destroy momentum, and alignment happens when teams solve one business problem together instead of defending their department badge.

Fear is often a signal that an opportunity is real, and saying yes can create the career path you never could have planned.

In the AI era, differentiation comes from value creation and tool mastery, not from having access to the same software everyone else has.

Closing Thoughts

This episode is a masterclass in practical leadership, the kind built in stores, boardrooms, and crisis moments, not in slogans. Harrison Lewis shows that transformation is not a tech upgrade, it is a human alignment problem anchored in outcome, strategy, and accountability. If you are leading change right now, this conversation will give you a cleaner playbook and a better mindset for the hard days.

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