21st Century Entrepreneurship

Matt Edelman is the CEO and president of Super League Enterprise and we spoke about how the gamified consumer mindset is reshaping marketing—and how his own path from Marvel and film production unexpectedly prepared him to run a public company. He describes producing a film as surprisingly close to running a business: “a film is like a mini company,” with budgets, contracts, marketing, distribution, and production all moving at once.
When the pandemic eliminated the live-event foundation of the company’s esports model, Matt first led a commercial pivot and later, as CEO, a much harder corporate turnaround under severe time pressure. His operating principle became simple: “you actually have choice. You have agency.” In negotiations, he recommends focusing on the other party’s motivations rather than your own emotions. The same thinking shapes his approach to marketing: understand a player mindset built around progress, status and rewards, then create participation with real value. One campaign linked digital mini-games to 30,000 free-popcorn coupons, all claimed on the first day—evidence, he says, that consumers “expect participatory rewards.”
For entrepreneurs and marketers, the practical value is a framework for handling pressure, designing stronger engagement, and treating gaming as a serious customer-acquisition channel across ages and business models.
Key takeaways
Build engagement around progress, status, rewards, and repeated participation.
When pressure spikes, make one daily decision that improves the situation.
In negotiations, focus first on the other party’s needs and motivations.
Use participatory rewards connecting digital actions to real-world value.
Treat gaming as a channel for B2C and B2B customer acquisition.
Don’t let emotion drive decisions when the business is cornered.

Dean Drako is a serial entrepreneur who has founded somewhere between five and fifteen companies, raised venture capital around 20 times in Silicon Valley, and experienced both successful exits and failures—and we spoke about the operating lessons that came from building repeatedly. His starting principle is simple: find a pain point you understand deeply, make sure many other people share it, and build what he calls “painkillers, not vitamins.” 
Dean also breaks company growth into three distinct organizational phases. From roughly one to 25 people, almost everyone knows everything; as the company grows, specialization and communication systems become necessary; and around 500 employees, the CEO must increasingly delegate and trust major leaders rather than rely on founder-level visibility. Timing can be just as decisive: one of his businesses spent roughly five years “pushing a rock uphill” because the market was not yet ready, reinforcing his view that success depends on product, timing, and some luck. 
That long-term perspective also explains why Dean says, “I don’t believe in an exit.” Instead of optimizing a company for sale, he argues founders should build durable products, customer relationships, revenue, service, and leadership—while staying adaptable enough to move quickly when customers and markets change. For Dean, the larger purpose is practical as well: his current work is organized around making physical spaces safer. 
For listeners, the value is a concrete framework for choosing problems, scaling leadership, surviving bad timing, and building for endurance rather than an exit.
Key takeaways
 Solve painful problems you personally understand and many customers actually share. 
 Expect leadership needs to change dramatically as headcount grows. 
 Around 500 employees, delegate major functions to trusted leaders. 
 Being years early can be as dangerous as having the wrong product. 
 Treat product, timing, and luck as separate ingredients of success. 
 Build enduring customer value instead of optimizing for an exit.

Sam Demma is an author and keynote speaker who has delivered close to 850 talks across four continents, and we spoke about how losing a Division I soccer scholarship after three major knee injuries redirected his life toward service, storytelling, and speaking. A teacher challenged him to take small, consistent action, which led to five and a half years of community cleanups—and eventually to schools inviting him back as a paid speaker.
Sam also explains the practical principles behind his latest work: “assume positive intent” when customers, colleagues, or family frustrate you, and “self-evaluate first” before blaming someone else when things go wrong. He describes great speaking as combining useful ideas with humor and stories, while his broader philosophy is that “when you give expecting nothing in return, beautiful things unfold.”
That philosophy is operational inside his company: 10% of annual profits after tax and expenses goes toward giving—half through books donated in January and half through cash donations in July, when charities may receive less support. For Sam, the purpose is less about what comes back financially and more about fulfillment, relationships, and knowing the work genuinely helps people.
Listeners leave with practical ways to manage reactions, take ownership, communicate memorable ideas, and make generosity part of how they operate.
Key takeaways
Assume positive intent before reacting to an angry customer or difficult email.
Self-evaluate your contribution before blaming someone when something goes wrong.
Turn personal experiences into stories that help people change their behavior.
Wrap useful information in humor and storytelling so people remember it.
Build generosity into business intentionally instead of treating it as an afterthought.
Small, consistent acts of service can open completely unexpected career paths.
Learn more about Return on Goodwill and Sam’s live events at https://samdemma.com/rog-live/

Amy Perez, CEO at myDream & Mindful Software, Inc. - is a former government technology leader, Lean Six Sigma Black Belt, and responsible AI consultant, and we spoke about using dreams as another source of information for decisions, self-awareness, and major life changes. After years managing high-stakes programs - including a digital system that distributed more than $10 billion in pandemic aid - Amy was outwardly successful but exhausted: “I was slowly dying on the inside.” A violent dream centered on her work phone became the turning point. She quit, immediately felt “20 pounds lighter,” and she and her husband left their careers to travel the world for a year. 
That experience led Amy into systematic dream interpretation and eventually to building an AI-assisted approach that combines emotions, common symbolism, personal meanings, recurring patterns, and input from experts with more than 180 years of combined clinical experience. What previously took Amy more than an hour can now be explored in seconds, but she stresses that interpretations should offer possibilities rather than dictate decisions. Her philosophy is equally clear about AI: “Technology should serve the person, not turn the person into the product.” Privacy, anonymization, expert review, and keeping human judgment in the loop are therefore central to the approach.
For listeners, the practical starting point is simple: set an intention before sleep, record whatever you remember - even one word - and begin looking for recurring emotional and symbolic patterns over time. Amy created myDream to make that process easier: the app lets users capture dreams quickly, add their own personal meanings to symbols, explore expert-informed interpretations, and identify recurring trends that may connect to waking life. Her larger argument is especially relevant to entrepreneurs: “you will never have all of the data you need to make a decision,” so dreams and intuition can become an additional source of information- not a replacement for judgment, but another signal worth learning how to hear.
Set an intention before sleep to strengthen dream recall.
 Record dreams immediately, even if you remember only one word. 
 Track recurring symbols alongside your personal emotional context. 
 Treat dream interpretations as possibilities, not instructions. 
 Use subconscious insights as additional data for difficult decisions. 
 Design personal AI around privacy, reflection, and human judgment.
Learn more about Amy’s work and explore myDream: https://www.mydream.io/

Saurabh Gupta is CEO of The Modern Data Company, and we spoke about why so many AI initiatives struggle to scale despite massive investment in the technology. His perspective comes from nearly 30 years in data: designing the World Bank’s open data platform, spending 12 years leading statistical data at the IMF, serving as Chief Data Officer for Washington, DC, and later working across more than 20 major enterprise data initiatives at Thoughtworks. Across those environments, he kept seeing the same pattern: “people are not focusing on outcomes, people are focusing on technologies.”
Saurabh explains why “bad data leads to bad AI,” and why adding more compute cannot fix a weak data foundation. His approach starts with right-to-left thinking: “bring only the minimum data that you need to solve a problem,” then expand as new problems emerge. He describes one manufacturer planning seven to eight quarters of foundational work before his team delivered the first use case and supporting platform in less than one quarter. Instead of stitching together 10–12 specialized tools, the method combines ingestion, quality, governance, orchestration, transformation and cataloging while keeping context attached to the data itself. Complex customer problems, he says, can often move from months to roughly four or five weeks.
For listeners building with AI, the practical lesson is simple: start with the outcome, minimize the data and infrastructure required, prove value quickly, and only then expand.
Key takeaways
Start with the business outcome before choosing technologies or infrastructure.
Bring only minimum necessary data, then expand as adjacent problems emerge.
Replace 10–12 disconnected tools with a unified ingestion-to-cataloging layer.
Package context, governance, lineage, and transformations with each data product.
Track unused pipelines and shut them down to stop wasted compute.
Aim to prove complex use cases in four to five weeks, not months.