The episode opens with a cluster of ad reads and network cross-promos — Just Food for Dogs, Kohler Cast Iron, and the EarSay audiobook club — before Jay Shetty cuts in with the main event. He welcomes Lucy Guo, describing her as a co-founder of Scale AI, an active investor, and one of the world's youngest self-made female billionaires. In a warm, personal opener, Jay traces their overlapping social orbits: the Luminara yacht trip, a fundraiser at Jay's home, the Dior party, and an upcoming Cannes rendezvous ^{[1] — Lucy Guo "College is outdated as an education system, but it remains the single best place to build deep emotional connections. It is the only time i…" 03:33 Play} . The tone is instantly informal and friendly, signaling the candid conversation that follows.

Jay opens with the question: what advice about success is most outdated? Lucy pivots straight to college, arguing that degrees are increasingly irrelevant in an AI-driven world. But she sidesteps the anti-college camp entirely by making a more precise claim — that 1–2 years of college is 'extremely useful' precisely because it is the only moment in life when strangers are universally open to deep emotional connection ^{[1] — Lucy Guo "College is outdated as an education system, but it remains the single best place to build deep emotional connections. It is the only time i…" 03:33 Play} . She describes hiring her own Carnegie Mellon TA and computer science 'little' based on relationships built in class, and investing in friends she met at hackathons across the US. The throughline is practical: college is not about coursework, it is about building the emotional foundation for a lifetime of sales, retention, and recruitment.

Jay opens with the question: what advice about success is most outdated? Lucy pivots straight to college, arguing that degrees are increasingly irrelevant in an AI-driven world. But she sidesteps the anti-college camp entirely by making a more precise claim — that 1–2 years of college is 'extremely useful' precisely because it is the only moment in life when strangers are universally open to deep emotional connection ^{[1] — Lucy Guo "College is outdated as an education system, but it remains the single best place to build deep emotional connections. It is the only time i…" 03:33 Play} . She describes hiring her own Carnegie Mellon TA and computer science 'little' based on relationships built in class, and investing in friends she met at hackathons across the US. The throughline is practical: college is not about coursework, it is about building the emotional foundation for a lifetime of sales, retention, and recruitment.

Jay asks about habits ambitious people think will help them succeed but actually hold them back. Lucy's answer is counterintuitive: the habit of gathering mentors. Smart people poll everyone around them, then freeze when the advice conflicts — or, in Lucy's case, when every mentor unanimously says the same thing. She describes the moment she faced the choice between staying at Snapchat (on a 10-20-30-40 vesting schedule, about to IPO, with a 10-person special team) and leaving to start a company. Every mentor said stay. She left anyway, optimizing for learning rather than money, and what followed became Scale AI ^{[1] — Lucy Guo "Every single mentor told Lucy to stay at Snapchat. She left anyway, and built Scale AI. The lesson: gathering too many mentors creates deci…" 06:54 Play} . The lesson she crystallises: make an imperfect decision and move, because a correctable path beats a paralysed one every time.

Jay asks about habits ambitious people think will help them succeed but actually hold them back. Lucy's answer is counterintuitive: the habit of gathering mentors. Smart people poll everyone around them, then freeze when the advice conflicts — or, in Lucy's case, when every mentor unanimously says the same thing. She describes the moment she faced the choice between staying at Snapchat (on a 10-20-30-40 vesting schedule, about to IPO, with a 10-person special team) and leaving to start a company. Every mentor said stay. She left anyway, optimizing for learning rather than money, and what followed became Scale AI ^{[1] — Lucy Guo "Every single mentor told Lucy to stay at Snapchat. She left anyway, and built Scale AI. The lesson: gathering too many mentors creates deci…" 06:54 Play} . The lesson she crystallises: make an imperfect decision and move, because a correctable path beats a paralysed one every time.

Jay asks about habits ambitious people think will help them succeed but actually hold them back. Lucy's answer is counterintuitive: the habit of gathering mentors. Smart people poll everyone around them, then freeze when the advice conflicts — or, in Lucy's case, when every mentor unanimously says the same thing. She describes the moment she faced the choice between staying at Snapchat (on a 10-20-30-40 vesting schedule, about to IPO, with a 10-person special team) and leaving to start a company. Every mentor said stay. She left anyway, optimizing for learning rather than money, and what followed became Scale AI ^{[1] — Lucy Guo "Every single mentor told Lucy to stay at Snapchat. She left anyway, and built Scale AI. The lesson: gathering too many mentors creates deci…" 06:54 Play} . The lesson she crystallises: make an imperfect decision and move, because a correctable path beats a paralysed one every time.

Reflecting on what gave her the inner confidence to leave Snap and forfeit most of her vesting equity, Lucy credits environment above all else: she was surrounded by Thiel Fellows who had already raised tens of millions or built billion-dollar companies ^{[1] — Lucy Guo "You cannot build a venture-scale business without genuinely believing you will be in the 0.01% that creates a unicorn. This is not arroganc…" 09:08 Play} . In that context, ambition recalibrated. She articulates a clear mental model: venture-scale businesses demand a founder who sincerely believes they are one of the 0.01% that will build a unicorn — not as posturing, but as an operating assumption. She distinguishes this from lifestyle businesses, noting that a $10M-per-year personal business is entirely achievable without delusion, but shooting for a billion-dollar outcome requires a different psychological gear entirely.

Jay poses a classic reset scenario: you're 22, broke, unknown — what do you do? Lucy's answer is both practical and slightly audacious: she actually did this. She lived on Menlo Avenue near USC, hung out at Stanford as if she were enrolled, and regularly attended hackathons like MHacks, PennApps, and HackMIT. She explains the logic clearly — hackathons self-select for people who would rather build something over the weekend than party, and a single event draws ambitious students from dozens of universities ^{[1] — Lucy Guo "Hang out at college campuses, attend hackathons, and blend in. Hackathons attract exactly the kind of people who spend weekends building in…" 09:58 Play} . She also reveals the origin story of her failed DoorDash clone, launched through campus infiltration, and why it ultimately didn't matter that the real DoorDash beat her to it.

Jay poses a classic reset scenario: you're 22, broke, unknown — what do you do? Lucy's answer is both practical and slightly audacious: she actually did this. She lived on Menlo Avenue near USC, hung out at Stanford as if she were enrolled, and regularly attended hackathons like MHacks, PennApps, and HackMIT. She explains the logic clearly — hackathons self-select for people who would rather build something over the weekend than party, and a single event draws ambitious students from dozens of universities ^{[1] — Lucy Guo "Hang out at college campuses, attend hackathons, and blend in. Hackathons attract exactly the kind of people who spend weekends building in…" 09:58 Play} . She also reveals the origin story of her failed DoorDash clone, launched through campus infiltration, and why it ultimately didn't matter that the real DoorDash beat her to it.

In a vivid origin story, Lucy describes building a food delivery platform at a hackathon and then pivoting to a home-cook delivery variant (illegal, she notes cheerfully) across college campuses. Between the hackathon and her company launch, the real DoorDash appeared. Her response was entirely pragmatic: 'Okay cool, whatever. Let me think of something else.' ^{[1] — Lucy Guo "When Lucy Guo built DoorDash at a hackathon and then saw the real DoorDash launch before her, she felt zero resentment. Ideas are everywher…" 12:45 Play} She frames this as a fundamental principle — billions of people have ideas all the time, and now with AI tools like Lovable and Replit, anyone can spin one up cheaply. What separates success from a wasted idea is the willingness to execute, iterate, and not mourn concepts that others got to first. The real DoorDash lesson was not loss; it was confirmation that she had good instincts worth deploying elsewhere.

Jay asks where young people waste most of their time building a career. Lucy's answer is pointed: they stay at big companies, collecting eight-figure equity packages that grow slightly every time they try to leave. She describes watching genuinely unicorn-caliber engineers remain at Snapchat for ten years because the golden handcuffs kept tightening ^{[1] — Lucy Guo "The most talented people Lucy knows are still at Snapchat after 10 years, collecting eight-figure equity packages while trading away the ch…" 13:52 Play} . She frames it without judgment — generational wealth is real, and the tradeoff is legitimately hard — but makes clear the true cost: that one passion project, that company they always wanted to build, traded away quietly for cash. She follows with a contrarian reframe: even if your startup fails, if you are talented, someone will acquire the company above its investor-valuation just to get you, because top talent is that scarce.

The conversation turns to speed as a competitive advantage. Jay notes that founders spend months obsessing over logos and fonts before a single user has signed up. Lucy agrees, and dismantles the perfectionism argument systematically: users tolerate bad UX if the product delivers real value (she uses a buggy iPhone payment flow as an example) ^{[1] — Lucy Guo "People tolerate bad UX if they want the product badly enough. You can design 90% of good UX in one to two days. Ship it, see if anyone buys…" 17:07 Play} . Her prescription is precise: design 90% of good UX in a day or two, build it, ship it, and only invest further if you see adoption. For B2B SaaS, she recommends launching a landing page and running calls to get LOIs signed before writing a line of code. For D2C, sell the product before it exists and compensate late orders with a free bonus item. The test is always demand, never craft. And perfectionism, she says plainly, is almost always just an excuse.

The conversation turns to speed as a competitive advantage. Jay notes that founders spend months obsessing over logos and fonts before a single user has signed up. Lucy agrees, and dismantles the perfectionism argument systematically: users tolerate bad UX if the product delivers real value (she uses a buggy iPhone payment flow as an example) ^{[1] — Lucy Guo "People tolerate bad UX if they want the product badly enough. You can design 90% of good UX in one to two days. Ship it, see if anyone buys…" 17:07 Play} . Her prescription is precise: design 90% of good UX in a day or two, build it, ship it, and only invest further if you see adoption. For B2B SaaS, she recommends launching a landing page and running calls to get LOIs signed before writing a line of code. For D2C, sell the product before it exists and compensate late orders with a free bonus item. The test is always demand, never craft. And perfectionism, she says plainly, is almost always just an excuse.

The episode pauses for a mid-show ad block. Kal Penn promotes EarSay, the Audible and iHeart audiobook club, featuring a conversation with the narrator of Project Hail Mary. T-Mobile advertises its network and T-Satellite service with an outdoor adventure vignette, and Dr. Laurie Santos promotes Simple Mills almond flour crackers as habit-supporting snacks, citing strategies from her Happiness Lab podcast.

Jay asks how you evaluate a risk when you cannot see the outcome. Lucy's answer is disarmingly simple: she uses two and only two criteria. First, is this life-changing? If not, the opportunity cost of her time is too high. Second, is she optimizing for learning? If neither answer is yes, she passes entirely. She illustrates this with her own Snapchat departure — the equity she forfeited was not life-changing to her at that stage, and what she would gain in founder knowledge would unlock far greater future earning power regardless of whether Scale AI succeeded ^{[1] — Lucy Guo "Lucy Guo evaluates every major decision with just two questions: Is this life-changing? And am I optimizing for learning? If neither answer…" 23:50 Play} . She broadens the point to general career logic: talented people consistently underestimate how much the market will pay for demonstrated excellence, and even failed startups attract acqui-hires above valuation. The framework reframes risk not as a gamble but as a structured trade of short-term certainty for long-term compounding.

Jay shares his own experience of feeling trapped in a consulting job that paid £45,000 a year, and how companies expertly manufacture the fear that leaving means downgrading your life. Lucy's response cuts to the root: 'People's fear is overcoming their logic.' ^{[1] — Lucy Guo "People's fear is overcoming their logic." 26:58 Play} The rational case for leaving is almost always clear — if the company valued you, they would rehire you, or another company will fight to pay you more. But logic cannot override a culture of manufactured scarcity. Her solution was accidental: she happened to be in a room with founders and investors who demystified the game. Investors told her, off the record, that 'exploding term sheets' were mostly bluffs. Peer founders showed her what the other side actually looked like. The network did not just create opportunity; it corrected the emotional distortion that kept talented people stuck.

Jay shares his own experience of feeling trapped in a consulting job that paid £45,000 a year, and how companies expertly manufacture the fear that leaving means downgrading your life. Lucy's response cuts to the root: 'People's fear is overcoming their logic.' ^{[1] — Lucy Guo "People's fear is overcoming their logic." 26:58 Play} The rational case for leaving is almost always clear — if the company valued you, they would rehire you, or another company will fight to pay you more. But logic cannot override a culture of manufactured scarcity. Her solution was accidental: she happened to be in a room with founders and investors who demystified the game. Investors told her, off the record, that 'exploding term sheets' were mostly bluffs. Peer founders showed her what the other side actually looked like. The network did not just create opportunity; it corrected the emotional distortion that kept talented people stuck.

Jay asks Lucy about the conventional 'find your passion' advice. She pushes back with a more pragmatic and arguably kinder model: optimize for impact in what you are best at, and use the fruits of that labor to stay close to what you love ^{[1] — Lucy Guo "Lucy Guo evaluates every major decision with just two questions: Is this life-changing? And am I optimizing for learning? If neither answer…" 23:50 Play} . She uses herself as the example — she loves nonprofits and charities, but realized she is world-class at making money, not running foundations. So she donates generously and backs people who actually know how to run organizations, rather than starting something she would do poorly. Music followed a similar path: she discovered DJing as a passion she could actually pursue without needing to be a singer. The conversation broadens into an honest acknowledgment that in many fields — music, acting — talent is insufficient on its own, and luck and network matter enormously. The takeaway is freeing: your career and your passion do not need to be the same thing.

Jay asks Lucy about the conventional 'find your passion' advice. She pushes back with a more pragmatic and arguably kinder model: optimize for impact in what you are best at, and use the fruits of that labor to stay close to what you love ^{[1] — Lucy Guo "Lucy Guo evaluates every major decision with just two questions: Is this life-changing? And am I optimizing for learning? If neither answer…" 23:50 Play} . She uses herself as the example — she loves nonprofits and charities, but realized she is world-class at making money, not running foundations. So she donates generously and backs people who actually know how to run organizations, rather than starting something she would do poorly. Music followed a similar path: she discovered DJing as a passion she could actually pursue without needing to be a singer. The conversation broadens into an honest acknowledgment that in many fields — music, acting — talent is insufficient on its own, and luck and network matter enormously. The takeaway is freeing: your career and your passion do not need to be the same thing.

Jay asks how Lucy separates self-worth from net worth. Her answer is unexpectedly geographic and personal: Miami. After years in San Francisco, LA, and New York — cities where, she says, people always wanted to know your title or your funding round — Miami offered something different. Strangers in elevators commented on her energy. Nobody asked what she did ^{[1] — Lucy Guo "Miami was the first time in my life where no one asked what I did and they just wanted to vibe with me because I gave off good energy." 31:45 Play} . For the first time, she experienced connection completely untethered from status. She traces the shift to rekindled family relationships and the simple pleasure of meeting people who just want to dance. It is a quietly important admission from someone who has achieved extraordinary financial success: the thing that actually made her confident was not the exit — it was being seen as a person first.

Jay probes Lucy on resilience and wealth. On failure and rejection, she is matter-of-fact: she describes being wined and dined by a top VC, feeling certain she would get a term sheet, and then receiving a sudden no out of nowhere. Her response was not devastation — it was a shrug and a pivot to the next lead. She credits relentless optimism and an allergy to victimhood as the traits that have carried her furthest. On money, she is equally honest: it removed the minor stresses she never found particularly stressful anyway (she loved couch-surfing), but introduced a subtler danger — the comfort that dulls hunger. The best founders, she implies, find ways to stay hungry even when the financial pressure disappears.

Jay asks what Lucy has learned from being in rooms with the world's most successful entrepreneurs. Her answer is genuinely counterintuitive: they are not the most educated, and they definitely do not understand how their industry is 'supposed to work' ^{[1] — Lucy Guo "The people at the top are not the most educated. They succeed because they lack the mental frameworks that tell everyone else something is …" 34:35 Play} . She argues this is causal, not coincidental. Airbnb founders were not real estate experts. Uber founders were not transportation experts. Industry veterans carry decades of received wisdom that tells them what is impossible — and that wisdom becomes a ceiling. Framework-free founders tell their PhD employees 'that's not impossible, go do it,' and they create incentives (bonuses, promotions) that force people to break the limits they thought were fixed. Jay punctuates this with the Steve Jobs / Steve Wozniak orchestra anecdote — Jobs succeeded not because he could code or design, but because he could orchestrate — and Lucy extends it to Elon Musk and SpaceX as modern proof points.

Jay asks what Lucy has learned from being in rooms with the world's most successful entrepreneurs. Her answer is genuinely counterintuitive: they are not the most educated, and they definitely do not understand how their industry is 'supposed to work' ^{[1] — Lucy Guo "The people at the top are not the most educated. They succeed because they lack the mental frameworks that tell everyone else something is …" 34:35 Play} . She argues this is causal, not coincidental. Airbnb founders were not real estate experts. Uber founders were not transportation experts. Industry veterans carry decades of received wisdom that tells them what is impossible — and that wisdom becomes a ceiling. Framework-free founders tell their PhD employees 'that's not impossible, go do it,' and they create incentives (bonuses, promotions) that force people to break the limits they thought were fixed. Jay punctuates this with the Steve Jobs / Steve Wozniak orchestra anecdote — Jobs succeeded not because he could code or design, but because he could orchestrate — and Lucy extends it to Elon Musk and SpaceX as modern proof points.

Jay asks what Lucy has learned from being in rooms with the world's most successful entrepreneurs. Her answer is genuinely counterintuitive: they are not the most educated, and they definitely do not understand how their industry is 'supposed to work' ^{[1] — Lucy Guo "The people at the top are not the most educated. They succeed because they lack the mental frameworks that tell everyone else something is …" 34:35 Play} . She argues this is causal, not coincidental. Airbnb founders were not real estate experts. Uber founders were not transportation experts. Industry veterans carry decades of received wisdom that tells them what is impossible — and that wisdom becomes a ceiling. Framework-free founders tell their PhD employees 'that's not impossible, go do it,' and they create incentives (bonuses, promotions) that force people to break the limits they thought were fixed. Jay punctuates this with the Steve Jobs / Steve Wozniak orchestra anecdote — Jobs succeeded not because he could code or design, but because he could orchestrate — and Lucy extends it to Elon Musk and SpaceX as modern proof points.

Jay asks what Lucy has learned from being in rooms with the world's most successful entrepreneurs. Her answer is genuinely counterintuitive: they are not the most educated, and they definitely do not understand how their industry is 'supposed to work' ^{[1] — Lucy Guo "The people at the top are not the most educated. They succeed because they lack the mental frameworks that tell everyone else something is …" 34:35 Play} . She argues this is causal, not coincidental. Airbnb founders were not real estate experts. Uber founders were not transportation experts. Industry veterans carry decades of received wisdom that tells them what is impossible — and that wisdom becomes a ceiling. Framework-free founders tell their PhD employees 'that's not impossible, go do it,' and they create incentives (bonuses, promotions) that force people to break the limits they thought were fixed. Jay punctuates this with the Steve Jobs / Steve Wozniak orchestra anecdote — Jobs succeeded not because he could code or design, but because he could orchestrate — and Lucy extends it to Elon Musk and SpaceX as modern proof points.

The conversation turns to the friction Lucy encountered while building Scale AI. Her answer centers on hiring philosophy rather than any specific crisis. At Scale, no role had firm boundaries: engineers labeled data, talked to creators, handled customer support ^{[1] — Lucy Guo "The people at the top are not the most educated. They succeed because they lack the mental frameworks that tell everyone else something is …" 34:35 Play} . Investors showed up and helped hire on LinkedIn — and cleaned the office when nobody had time. This all-hands-on-deck culture was intentional, because she believes deep customer contact is the only reliable way to know what to build next, and leaders insulated from that contact lose their edge. She acknowledges this environment is polarizing — some hires push back on being asked to do things 'outside their job description' — but says the right people embrace it, and that self-selection is actually one of the most useful filters you have.

The conversation turns to the friction Lucy encountered while building Scale AI. Her answer centers on hiring philosophy rather than any specific crisis. At Scale, no role had firm boundaries: engineers labeled data, talked to creators, handled customer support ^{[1] — Lucy Guo "The people at the top are not the most educated. They succeed because they lack the mental frameworks that tell everyone else something is …" 34:35 Play} . Investors showed up and helped hire on LinkedIn — and cleaned the office when nobody had time. This all-hands-on-deck culture was intentional, because she believes deep customer contact is the only reliable way to know what to build next, and leaders insulated from that contact lose their edge. She acknowledges this environment is polarizing — some hires push back on being asked to do things 'outside their job description' — but says the right people embrace it, and that self-selection is actually one of the most useful filters you have.

A second advertising break features Kal Penn promoting EarSay with the Audible original romantic comedy Just Kiss Already, performed by Simu Liu and Philippa Soo. T-Mobile repeats its network and free phone offer, and Dr. Laurie Santos returns with another Simple Mills promotion, this time emphasising Pop'ems cheese-puffed crackers alongside the almond flour crackers.

Jay asks what three human skills will survive the AI wave. Lucy's answers are precise and unconventional: first, emotional intelligence and the capacity for human connection — AI can draft a contract but cannot close a deal with warmth; second, taste — the ability to select the best output from a hundred AI-generated options; third, and most urgently, the ability to scale your own time using AI before the rest of the market catches up ^{[1] — Lucy Guo "AI can negotiate contracts and write code, but it cannot close a deal with emotional warmth, and it cannot pick the best design from 100 ge…" 44:40 Play} . She then pivots to hiring, revealing that every role in her current companies — including non-technical account executives — now involves a weekend take-home where candidates must use tools like Cursor, Replit, or Claude Code to build a functional bot from scratch. She is not testing output; she is measuring learning velocity, because a fast learner who did not know these tools on Friday can be a 10x employee by Monday, and that potential outweighs years of static experience.

Jay asks what three human skills will survive the AI wave. Lucy's answers are precise and unconventional: first, emotional intelligence and the capacity for human connection — AI can draft a contract but cannot close a deal with warmth; second, taste — the ability to select the best output from a hundred AI-generated options; third, and most urgently, the ability to scale your own time using AI before the rest of the market catches up ^{[1] — Lucy Guo "AI can negotiate contracts and write code, but it cannot close a deal with emotional warmth, and it cannot pick the best design from 100 ge…" 44:40 Play} . She then pivots to hiring, revealing that every role in her current companies — including non-technical account executives — now involves a weekend take-home where candidates must use tools like Cursor, Replit, or Claude Code to build a functional bot from scratch. She is not testing output; she is measuring learning velocity, because a fast learner who did not know these tools on Friday can be a 10x employee by Monday, and that potential outweighs years of static experience.

Jay asks what three human skills will survive the AI wave. Lucy's answers are precise and unconventional: first, emotional intelligence and the capacity for human connection — AI can draft a contract but cannot close a deal with warmth; second, taste — the ability to select the best output from a hundred AI-generated options; third, and most urgently, the ability to scale your own time using AI before the rest of the market catches up ^{[1] — Lucy Guo "AI can negotiate contracts and write code, but it cannot close a deal with emotional warmth, and it cannot pick the best design from 100 ge…" 44:40 Play} . She then pivots to hiring, revealing that every role in her current companies — including non-technical account executives — now involves a weekend take-home where candidates must use tools like Cursor, Replit, or Claude Code to build a functional bot from scratch. She is not testing output; she is measuring learning velocity, because a fast learner who did not know these tools on Friday can be a 10x employee by Monday, and that potential outweighs years of static experience.

Jay asks which jobs that look safe today will vanish in ten years. Lucy points to creative roles that feel distinctly human — photography, videography — arguing that most models will be AI-generated, with humans primarily licensing their likeness ^{[1] — Lucy Guo "A great engineer given AI tools becomes a 100x engineer. An entry-level engineer given the same tools just generates more technical debt. A…" 48:05 Play} . Writing will shrink too, though editing will persist. Then Jay asks about the biggest mistake people are making with AI right now. Lucy's answer is sharp: they assume it works. AI hallucinates. It generates one bug and then confidently builds ten more on top of it. A great engineer catches those errors and overrides them, becoming a 100x operator. An entry-level engineer just accepts the output, and the result is compounding technical debt. The asymmetry is profound: AI is an amplifier of existing quality, not a democratizer of quality itself.

Jay asks whether Lucy always knew she would be this successful. Her answer reveals the roots of something deeper than ambition: she has always loved building things and watching people use them. In 5th or 6th grade she convinced engineers, designers, and artists to build a virtual pet site for free ^{[1] — Lucy Guo "When Lucy Guo built DoorDash at a hackathon and then saw the real DoorDash launch before her, she felt zero resentment. Ideas are everywher…" 12:45 Play} . She made arcade game sites, Twitter bots, and WordPress tools. She submitted a 'really crappy website' to StumbleUpon and watched 10,000 people land on it, and that feeling — of a stranger using something she created — hooked her completely. She credits strict Asian parents for the unintentional gift: no sleepovers, no sports, no pets meant the internet became her playground. She reflects with humor on the path not taken: she had Olympic-level swimming and basketball skills, but the coding filled the gap, and she has no regrets.