Founder's Story

Daniel opens by asking why someone with nearly $2 billion in exits is not sitting on a beach sipping drinks. Tom Sosnoff, founder of thinkorswim, tastytrade, and LossDog’s answer is simple: building is what turns him on. From there, the conversation becomes a raw and funny look into the psychology of a builder who says he has no hobbies, no Netflix account, and has never ordered anything on Amazon. Tom shares the “no high fives” rule he and his partner Scott live by, why they never build companies to sell, how buyers found thinkorswim and tastytrade, and why he cares deeply that the companies who buy from him get an asset worth more than they paid.

The episode then moves into Tom’s newest company, LossDog, which gives people a number for their professional worth, and opens a broader conversation about wage gaps, negotiation, employee equity, tokenized private shares, prediction markets, and democratizing access to financial information. Tom also reflects on building one of the first digital financial media networks, why hiring comedians to explain finance failed, and why he and his friends ended up becoming the show themselves.

Key Discussion Points

Tom says there is no chance he would retire to a beach after big exits because he loves working, building, and creating more than anything else.

He says the question of work-life balance drives him crazy, describing himself as a “junkie” for work and still the first person in the office every day.

Tom jokes that he is “hobbyless” and says three things differentiate him: he has no hobbies, no Netflix account, and has never ordered anything on Amazon.

He explains the rule he and longtime partner Scott live by after exits: no high fives, no congratulations, because they do not see themselves as done.

Tom says they never build companies with the intention to sell. They build things they believe people need, and buyers eventually approach them when the timing is right.

When thinkorswim sold, Tom says multiple companies were bidding in cash, and when tastytrade sold, five companies emerged as potential buyers.

Tom says he did not choose buyers based only on the highest offer. He cared about whether the buyer would get a great company and a deal that would prove valuable over time.

He argues that his companies continue working after acquisition because the technology is strong enough that even mediocre operators can run it successfully.

Tom shares the origin of the LossDog name, explaining that it came from a “Loss Cat” poster he saw in a theater green room and loved so much that he tracked down the artist.

LossDog gives people a professional worth number, and Tom says his own calculated career value came out to $343,000, though he jokes that his resume and LinkedIn profile are not very strong.

Tom argues that context and information are incredibly valuable in negotiation, especially because executives have public compensation comparisons while average employees often lack the same visibility.

He says the wage gap in America is real and that the only way to help average employees is to give them better information, context, and education about what they are worth.

Tom says he is not building LossDog simply to solve a problem, but because it interests him and fits into a larger ecosystem of companies involving digitization, tokenization, prediction markets, and financial engines.

He discusses prediction markets, saying they are interesting and likely here to stay, but also believes current fee structures are too high and inefficient for the average individual.

Tom talks about buying private shares in companies before IPOs and predicts that future employee equity markets may become tokenized, creating lower-cost marketplaces for private company shares.

He shares that when he and Scott sold their companies, they gave $50 million in cash to employees on top of employee equity, including life-changing checks for some people.

Tom says giving someone a million-dollar check is one of the coolest things someone can do, and he would rather do that than buy luxury toys like yachts or cars.

He explains why he still does a daily show: he has a special relationship with the audience, he enjoys it, and he would rather do that than almost anything else.

Tom tells the story of creating tastytrade as a digital financial media company after selling thinkorswim because he disliked the state of traditional financial media.

The original plan was to hire comedians to make finance entertaining, but after months of testing, Tom realized they hated finance and were not funny together talking about it—so he and Tony took over the show themselves.

Takeaways

Tom’s version of success is not retirement. It is the ability to keep building things that interest him.

Great exits often come from building something genuinely valuable, not from building a company solely to sell it.

Information changes negotiation. Tom believes employees lose enormous lifetime earnings because they do not have the same compensation context executives do.

Legacy is not one company or one exit. For Tom, it includes the products built, the employees rewarded, the markets democratized, and the value left behind.

The future of private markets may be tokenized, giving employees and investors more transparent, lower-cost ways to trade private company equity before an IPO.

Closing Thoughts

Tom Sosnoff’s story is not the typical founder story about chasing an exit and disappearing. It is about obsession, repetition, and the joy of building again and again. From thinkorswim to tastytrade to LossDog, Tom has built companies that democratize access to financial tools, education, and information. This episode captures a founder who has already won by almost any financial measure, but still shows up because the work itself is the reward.

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Daniel opens by discussing the growing shortage of nurses and physicians across the United States and asks whether AI can realistically solve the problem. Raj Toleti, Chairman and CEO of Andor Health, explains that the shortage is already here and argues that automation is the only scalable path to democratizing healthcare, particularly for rural communities where clinicians are scarce. Rather than replacing doctors, Raj believes AI should eliminate administrative work, surface critical patient information, and assist clinicians so they can spend more time delivering care.

The conversation then explores Raj’s path from autonomous vehicle research and Microsoft into healthcare entrepreneurship, his family of physicians, building multiple healthcare companies, profitable exits, employee ownership, creating millionaires inside his businesses, mentoring young entrepreneurs, and why he continues building despite already achieving financial success.Key Discussion Points

Raj says the healthcare staffing crisis is not a future problem—it already exists today, with more nurses leaving the profession than entering it and ongoing shortages of specialists across the country.

He explains that AI should not replace clinicians but instead automate administrative work, retrieve patient records, summarize information, assist with documentation, and prepare physicians before they begin patient interactions.

Raj shares that Andor Health's AI is already reducing thousands of nursing hours while extending healthcare access into remote communities where clinicians are difficult to reach.

He believes trust in AI comes from knowing when to introduce a human into the workflow, describing a “human-in-the-loop” approach rather than fully autonomous healthcare.

Raj discusses how AI can identify language barriers, accessibility needs, documentation requirements, and clinical reasoning before a physician even joins the patient interaction.

He reflects on his engineering background, including autonomous vehicle research in the early 1990s, before deciding that healthcare automation would allow him to impact millions of people rather than treating dozens of patients individually.

Raj shares that he comes from a family with 33 clinicians, which made healthcare innovation feel like a natural calling despite choosing engineering over medicine.

He remembers joining Microsoft when his father had never even heard of the company, later leaving to pursue entrepreneurship despite the uncertainty.

Raj explains that one of his personal metrics is the number of jobs he creates, seeing entrepreneurship as a way to provide opportunity and improve lives far beyond his own success.

He admits that retirement lasted only about two months after selling his first company before realizing that building businesses was his true purpose.

Raj says every company he builds is designed to be profitable, financially resilient, and capable of delivering measurable customer outcomes rather than relying on outside funding alone.

He argues that entrepreneurs should prepare their companies for an exit every day—not because they plan to sell, but because strong financials, profitability, and customer value naturally create acquisition opportunities.

Raj shares that he has created numerous employee millionaires through stock option plans and believes educating employees about equity is just as important as granting it.

He emphasizes that stock ownership changes lives, but many employees fail to understand taxation, exercising options, and long-term wealth creation strategies.

Raj also discusses his internship program, explaining that many of his youngest interns eventually became senior executives and successful entrepreneurs after receiving early opportunities and mentorship.

Contrary to common stereotypes, Raj believes today's younger generation is highly motivated, provided they receive mentorship, confidence, and meaningful opportunities early in their careers.Takeaways

AI's greatest opportunity in healthcare is augmenting clinicians—not replacing them—by automating repetitive work while keeping humans responsible for patient care.

Profitable companies with strong customer outcomes are positioned to survive market cycles and create stronger long-term acquisition opportunities than businesses focused only on raising capital.

Employee ownership can create extraordinary wealth, but founders have a responsibility to educate employees about how equity actually works.

Mentorship compounds over decades. Raj's investment in interns and young professionals has produced executives, founders, and multiple employee millionaires.

Legacy is not measured by company valuations or awards—it is measured by the number of lives, careers, and patients positively impacted over time.

Closing Thoughts

Raj Toleti has spent his career building technology that scales human care rather than replacing it. From autonomous systems research to multiple healthcare exits and Andor Health's AI-powered clinical platform, his focus has remained remarkably consistent: use technology to help clinicians do what only humans can do best. This episode captures a founder who believes entrepreneurship is ultimately about outcomes—not just financial returns, but healthier patients, stronger companies, empowered employees, and lives changed at scale.

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Daniel and Lee Rossey, CTO and Co-Founder of SimSpace, open with the explosion of AI agent companies and the growing comfort people have with giving these systems access to business tools, financial data, credit cards, and personal information. Lee warns that the benefits are real, but so are the risks: every company eventually faces compromise, and users should assume that any sensitive data they feed into these tools could someday get exposed. From there, the conversation moves into agent-to-agent communication, governance, AI guardrails, MIT Lincoln Lab, bootstrapping SimSpace, cyber ranges, critical infrastructure, and the future of cybersecurity jobs in an AI-driven world.

Key Discussion Points

Lee explains that AI agents can create real productivity benefits, but users need to be honest about the risk of putting sensitive information into systems that may eventually leak or be hacked.

He compares the early AI-agent era to the early days of social media, when people shared everything first and only later realized the privacy and security consequences.

Lee says the AI boom has created real opportunity but also massive hype, with nearly every company now claiming to use AI agents regardless of whether the product is truly differentiated.

He explains that the future is not single-agent AI but multi-agent systems, where agents communicate with other agents and act on behalf of people or companies.

Once AI agents begin acting on someone’s behalf, Lee says the key questions become governance, controls, role-based access, boundaries, and guardrails.

Lee predicts a growing market around monitoring AI agents, preventing data leakage, controlling access, and keeping autonomous systems inside trusted lanes.

He shares his experience at MIT Lincoln Laboratory, where he worked on applied research tied to national security, including cyber defense, offensive cyber questions, DARPA-style technology, and government cyber capabilities.

Lee explains how he and his co-founder Hutch, an F-15 fighter pilot, tested their chemistry and technology through early projects before spinning SimSpace out of the lab.

He describes SimSpace’s bootstrapped early years, using government contracts, credibility, speed, and long nights to compete against large defense contractors and well-funded companies.

Lee explains why cyber ranges and digital twins matter: they allow organizations to model realistic environments, test defenses, train teams, and validate whether systems can withstand attacks.

He says AI has accelerated the urgency of SimSpace’s work because major companies cannot simply replace cybersecurity teams with autonomous agents without testing, vetting, and proving those agents are safe.

Lee explains that modern cybersecurity must assume breach. The real question is not whether someone can get in, but how fast a company can detect, respond, recover, and limit damage.

He warns that AI is being weaponized across the cyber kill chain, from finding vulnerabilities to mapping networks, moving laterally, communicating back to attackers, and executing a final objective.

The conversation also covers critical infrastructure, including power grids, airports, industrial systems, and operational technology, where attacks may be less about money and more about strategic disruption.

Lee believes cybersecurity will remain a hot field, but the jobs will change as AI automates some tasks and creates demand for people who can secure, architect, test, red-team, and govern AI-driven systems.

Takeaways

AI agents can be powerful, but the more access they receive, the more important governance, trust, monitoring, and access controls become.

People should treat sensitive AI inputs like they treat financial data: only share what they are comfortable potentially being exposed if the system or company is compromised.

Cybersecurity is moving toward a world where automated adversaries face automated defenses, but Lee believes humans still need to stay in the loop for governance and control.

Bootstrapped companies can beat larger incumbents when they have credibility, speed, focus, and a willingness to take on technical debt temporarily to win the market.

Critical infrastructure security is a national security issue, because attacks on power, transportation, water, or industrial systems can be used to create disruption at a strategic level.

Closing Thoughts

Lee Rossey’s story shows what happens when deep national security research meets entrepreneurship. SimSpace was built from years of applied cyber work at MIT Lincoln Laboratory, but the company’s relevance has only grown as AI agents, automation, and critical infrastructure threats move into the mainstream. This episode is a warning and a roadmap: AI will transform cybersecurity, but trust cannot be assumed. It has to be tested, modeled, governed, and proven before autonomous systems are allowed to defend—or act for—the world’s most important organizations.

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Daniel and Rana Gujral, CEO of Behavioral Signals, begin with the biggest misconception in AI: that the real debate is about capability. Rana argues that the more important question is not whether AI can write, reason, analyze, or outperform humans on benchmarks, but whether it is strengthening human instinct or quietly replacing it. From there, the conversation explores why enterprise AI often fails when companies use it as a headcount-reduction shortcut, why workers resist tools they fear will train their replacement, and why AI has to be built into redesigned workflows rather than bolted onto old processes. Rana also breaks down voice deepfakes, machine consciousness, artificial general experience, trusting intuition, the role of failure, and why being human is about creating meaning under constraint.

Key Discussion Points

Rana says the public AI conversation is focused on the wrong axis: instead of asking what AI can do, we should ask what using AI does to human attention, judgment, and instinct over time.

He explains that AI harm may not arrive as one dramatic rupture, but through quiet drift: defaults, recommendations, attention systems, and convenience slowly reshaping how people think.

Rana argues that many enterprise AI rollouts failed because companies believed in a “fantasy of substitution,” assuming they could drop a model into a workflow, remove people, and instantly book savings.

He says real work is full of exceptions, judgment calls, relationships, and context, and that AI often handles the middle of the workflow but fails at the edges where the real value lives.

Rana explains that employees may resist AI not because they are illiterate, but because nobody has answered what happens if the tool makes them more productive: more meaningful work, more workload, or replacement.

The conversation explores machine consciousness, with Rana warning that fluent language, empathy, memory, and personality can make systems feel conscious even when that may be human projection rather than evidence.

Rana introduces the idea of artificial general experience, arguing that the more practical question is whether machines develop stakes, preferences, and something that functions like caring about outcomes.

He says we are entering an era where “hearing is no longer believing,” because voice cloning tools can replicate someone’s voice from only a few seconds of audio.

Rana explains that older deepfake detection methods looked for imperfections in synthetic speech, but newer models are learning to patch those tells, making behavioral and temporal patterns more important.

He shares that Behavioral Signals focuses on how a specific person speaks over time, including cadence, articulation, co-articulation, and prosody patterns that are harder to fake consistently.

Rana reflects on leaving India after undergrad and walking into uncertainty, saying the biggest lesson was that life does not follow a clean formula and the future is far more unpredictable than we are taught.

He says one thing he wishes he had done earlier was trust his instincts, because intuition is not magic; it is accumulated experience compressed into a signal.

Rana explains that failure is not a detour from success but the road itself, because suffering and breakdowns reveal what someone values, what needs protection, and where their understanding ends.

He argues that a smart machine gives the right answer, but a machine that understands can explain why that answer holds, where it breaks, and what would have to be true for it to be wrong.

Rana shares his turnaround philosophy: the secret unlock is not a clever pivot, but radical honesty—naming the real problem in the room and giving people a concrete next action.

Takeaways

The biggest AI risk may not be replacement overnight. It may be the slow erosion of human judgment as people outsource thinking, framing, and decision-making to systems that feel helpful.

AI works best when companies redesign the workflow around human-machine collaboration instead of inserting a chatbot into old processes and expecting transformation.

Voice deepfakes are becoming a trust crisis, and Rana believes society will need to normalize verification, including callbacks, family code words, and skepticism under emotional pressure.

Human intuition should not automatically lose to spreadsheets. Rana sees intuition as pattern recognition built from experience, and analysis as a check—not a replacement.

Machines may become more intelligent, but understanding requires consequence, transformation, and the weight of experience—not just eloquent answers.

Closing Thoughts

Rana Gujral’s conversation is less about AI hype and more about what AI forces us to confront in ourselves. As machines become more fluent, more persuasive, and more integrated into our decisions, Rana argues that the real question is not whether they can think like humans, but whether humans will keep building judgment, meaning, and instinct of their own. This episode captures one of the deepest AI conversations on Founder’s Story: a warning about convenience, a framework for trust, and a reminder that being human means building meaning under constraint.

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Daniel and Lukas Kerrebijn, co-founder of RD Dubai, RD Vastgoed, and RD Advisory, trace the journey from a teenage intern questioning what real estate agents actually did, to building a platform connecting property sellers with investors, to expanding into Dubai when Dutch regulations made the local market harder for investors. Lukas explains how his first deal in the Netherlands revealed demand from investors, why Dubai became the next major opportunity, and how the RD Dubai brand evolved beyond transactions into community, events, sports sponsorships, and investor networks. The conversation also explores youth, boldness, talent, manifestation, Morocco, Abu Dhabi, and Lukas’s dream of using real estate and sports to create long-term impact.

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Key Discussion Points

Lukas shares the story of his first real estate deal at 19 in Vlaardingen, where he found a seller through social media campaigns and brought seven investors to view the property.

He admits he told the seller he was 25 because he was nervous about being taken seriously at 19, and the seller replied that he looked very young for his age.

That first deal opened his eyes to the possibility of building a real estate platform that connected sellers directly with investors and created faster transaction timelines.

Lukas explains that his early frustration came from seeing agents collect commissions in a hot Amsterdam market where properties were selling easily, leading him to question the traditional model.

He says starting young was an advantage because he had less responsibility, more time, and fewer fears shaped by previous business trauma.

Lukas describes how Dutch government rule changes made buy-to-let investing less attractive, reduced investor confidence, and pushed him to look for new markets.

He moved to Dubai initially to look for investment properties for himself and his business partner, but quickly discovered major demand from Dutch investors who also wanted access to the UAE market.

RD Dubai’s early advantage came from already having a trusted Dutch investor base, making it easier to guide those clients into Dubai real estate opportunities.

Lukas explains that sponsorships with Glory Kickboxing, Dutch football, and Formula One-related activities helped build brand awareness, attract talent, and align the company with ambition and sports culture.

He says the sponsorship strategy was not only about sales; it helped attract job applicants who matched the company’s brand DNA and contributed to a strong retention culture.

Lukas shares his long-term dream of building sports complexes for underprivileged children in Africa, starting with a project in Marrakech that combines real estate, wellness, sport, and social impact.

He believes Abu Dhabi may be one of the biggest real estate opportunities investors are missing right now because of major projects, coastal locations, and more attractive price-to-quality dynamics compared with Dubai.

Takeaways

Starting young can be a massive advantage because boldness, energy, and fewer obligations can help a founder move before fear takes over.

Regulation can completely reshape a market, and Lukas’s move from the Netherlands to Dubai shows how founders must adapt when the rules change.

Brand is not only for customers. RD Dubai’s sports sponsorships helped attract talent, build community, and create a company identity people wanted to be part of.

Real estate investing is not just about spreadsheets. Lukas argues that community, access, lifestyle, and long-term networks can create lifetime value for investors.

Manifestation matters to Lukas because every major move starts with a vision, and he believes the mind shapes what someone is willing to pursue.

Closing Thoughts

Lukas Kerrebijn’s story is about youth, conviction, and seeing opportunity before the market catches up. At 19, he saw inefficiency in Dutch real estate. At 23, he saw Dubai as the next move. Now, before 30, he is thinking beyond transactions and toward community, sports, wellness, Africa, and legacy. This episode captures a founder who is still early in his journey, but already building with the kind of ambition, boldness, and long-term vision that can turn one deal into an entire ecosystem.

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