Morning Walk With Murty

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This report profiles Aashna Doshi, a young software engineer who chose to resign from Google to launch her own technology company. Her transition into entrepreneurship was inspired by the unexpected success of her side project, a podcast titled “0 to 1” that gained significant traction on YouTube. Following this creative momentum, she co-founded Bounty, a startup focused on agentic AI that operates on a performance-based revenue model. Doshi’s journey highlights a shift from corporate financial stability toward the high-risk, high-reward landscape of the artificial intelligence sector. Ultimately, the source illustrates how modern digital platforms can provide the networking and confidence necessary for young professionals to pursue ambitious startup ventures.

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The Google Engineer Who Walked Away for an AI Startup | Morning Walk with Murty

00:00:00 Speaker 1: Um. February twenty twenty four was just an absolute bloodbath in the tech sector.

00:00:05 Speaker 2: So complete panic. I mean, mass layoffs, hiring freezes.

00:00:09 Speaker 1: Yeah, exactly. The headlines across Silicon Valley were basically doom and gloom. Getting your foot in the door literally anywhere was considered a massive win.

00:00:17 Speaker 2: Absolutely.

00:00:17 Speaker 1: So you have this twenty one year old software engineer Ash Narducci, and she actually lands a full time engineering offer from Google during all this.

00:00:24 Speaker 2: Which is incredible timing.

00:00:25 Speaker 1: Right? So the logical move there is you sign the paper, you pack your bags for California, and you just breathe a massive sigh of relief.

00:00:32 Speaker 2: Protect the win and run.

00:00:34 Speaker 1: Exactly. But instead she looks at the offer, tells Google, uh, actually, I prefer to live in New York City. And she effectively puts the most coveted safety net in the entire industry on the line just to negotiate her location.

00:00:47 Speaker 2: And the crazy part is she one.

00:00:49 Speaker 1: She actually won. Welcome to today's deep dive. Yeah. If you're tracking the, you know, the rapidly shifting tech landscape and you really want to cut through all the hype to find the actual signals of where the industry is going, you are in the right place.

00:01:02 Speaker 2: We have a really good one today.

00:01:03 Speaker 1: We do. Our mission today is to unpack this highly unusual career blueprint that we found hidden inside a really fascinating Times of India. Article dated June twenty seven, twenty twenty six. Because Asia's story didn't end with that Manhattan negotiation.

00:01:19 Speaker 2: Not even close.

00:01:20 Speaker 1: No. Fast forward to May twenty twenty six, and at twenty three years old, she intentionally walked away from that secure Google paycheck to launch a pre-revenue AI startup.

00:01:31 Speaker 2: Okay, let's unpack this because walking away from big tech right now to build a startup from scratch, I mean, that requires a very specific type of risk calculus.

00:01:39 Speaker 1: Yeah. It requires an engineer's approach to risk. Yeah. Which is exactly what makes this a masterclass in strategic maneuvering rather than just, you know, your typical founder burnout story, right?

00:01:48 Speaker 2: She wasn't just tired of corporate life.

00:01:50 Speaker 1: No, not at all. We aren't looking at someone who couldn't handle the corporate structure. We're looking at someone who viewed her career the exact same way she views a software architecture.

00:01:58 Speaker 2: Oh. That's interesting. Like looking for bugs? Basically, yeah. Identifying single points of failure, looking for bottlenecks and realizing that a quote unquote good situation can often be the exact barrier preventing a great one, that geography negotiation back in twenty twenty four, that was merely her first beta test of market leverage, right?

00:02:18 Speaker 1: Because she recognized her marginal utility. I mean, it's like holding a winning lottery ticket, but asking the cashier to change the payout terms.

00:02:25 Speaker 2: That is a great way to put it.

00:02:26 Speaker 1: It shows a highly unusual risk tolerance for twenty one or twenty two year old, because even in a terrible market, Google had already spent the resources to interview her, vet her, select her.

00:02:38 Speaker 2: They'd sunk the costs.

00:02:39 Speaker 1: Exactly. She calculated that the cost to Google of restarting that entire hiring pipeline was actually higher than the administrative friction of just placing her in the Manhattan office.

00:02:49 Speaker 2: It's a localized test of leverage, and it reveals a mindset that was operating completely outside of that standard scarcity panic that most junior engineers were feeling at the time.

00:02:59 Speaker 1: Yeah. Everyone else was just terrified to lose an offer.

00:03:01 Speaker 2: Yeah. Right. And what's fascinating here is how that initial success essentially trained her neural network. When you push back against a massive, seemingly rigid structure like Google and the structure actually bends.

00:03:15 Speaker 1: It changes everything.

00:03:16 Speaker 2: It completely shatters the illusion of the corporate monolith. You learn that the system is entirely malleable, assuming you can tolerate the discomfort of uncertainty.

00:03:25 Speaker 1: But the physical location of her desk was just the baseline, right? By early twenty twenty five, she's embedded in the New York office. She's surrounded by top tier talent, doing all this complex technical work, and she hits a very common wall for high performing engineers.

00:03:39 Speaker 2: Yeah. Isolation.

00:03:40 Speaker 1: Yes. She realized that writing code, while intellectually stimulating, is inherently an isolationist activity. I mean, you're basically optimizing communication with the compiler.

00:03:50 Speaker 2: Yeah. And she wanted to optimize communication with the broader industry.

00:03:53 Speaker 1: So she built a side channel. She teamed up with a fellow big tech engineer and launched a podcast called Zero to One, aiming to interview founders, creators and executives about their personal career trajectories.

00:04:04 Speaker 2: Which is such a smart pivot.

00:04:05 Speaker 1: It really is. Yeah, and I love the mechanical implication of that title. Beyond the obvious software engineering nod to binary state, you know, the literal zeros and ones of machine logic, right?

00:04:16 Speaker 2: The coding joke. Yeah, yeah.

00:04:18 Speaker 1: But it also taps into Peter Thiel's framework of progress. Going from one to N is just scaling something that already exists. It's horizontal.

00:04:27 Speaker 2: Like climbing the corporate ladder from junior engineer to senior engineer.

00:04:30 Speaker 1: Exactly. But going from zero to one is vertical progress. It's creating something entirely new. She was essentially using this podcast to crowdsource the mental frameworks of people who had successfully navigated those vertical leaps.

00:04:43 Speaker 2: She was treating those interviews as a data set. Yeah. Think about it. By constantly asking successful people how they navigated their own transition from a static state to a dynamic one. She was mapping the hidden pathways of the tech industry. Right. But the mechanism of how she built this project is where the real blueprint lies for anyone listening. Because this wasn't just like a casual weekend hobby that accidentally went viral. Not at all. It was a hyper efficient, highly engineered networking engine.

00:05:11 Speaker 1: Here's where it gets really interesting. Think about the standard cold outreach dynamic today.

00:05:17 Speaker 2: It's painful.

00:05:18 Speaker 1: It is. If you are a twenty two year old developer and you send a LinkedIn DM to some Microsoft VP asking for a fifteen minute virtual coffee chat to, quote unquote, pick their brain.

00:05:31 Speaker 2: Oh, please don't do.

00:05:32 Speaker 1: That. Right. You are basically asking for charity. You're introducing friction into their day with zero return on their investment. That message is instantly archived.

00:05:41 Speaker 2: Left on read forever.

00:05:42 Speaker 1: Forever. But Ashna and her co-host didn't ask for coffee. They spent a year bootstrapping this podcast entirely through cold direct messages, but they flipped the value proposition.

00:05:52 Speaker 2: They moved from value extraction to value creation.

00:05:55 Speaker 1: It was a Trojan horse. Instead of knocking on the door of the corporate castle begging for access, they basically built a stage outside and invited the executives to step onto it.

00:06:04 Speaker 2: And that stage got big.

00:06:05 Speaker 1: It got huge. The source material notes that within one year, this highly niche, career focused show surged past one hundred thousand views on YouTube.

00:06:14 Speaker 2: That metric right there completely changes the physics of cold outreach entirely. You are no longer a junior engineer asking a favor. You are a media operator offering distribution. You're offering these legacy executives a direct pipeline to tens of thousands of young, engaged tech professionals, which.

00:06:34 Speaker 1: Is an audience they desperately want for recruiting and brand positioning.

00:06:38 Speaker 2: Exactly. Distribution is the ultimate skeleton key in the modern economy. I mean, we often talk about networking as this nebulous activity of, uh, attending mixers or updating resumes.

00:06:50 Speaker 1: Handing out business.

00:06:51 Speaker 2: Cards. Right. But building a media asset, whether it's a hyper targeted video channel, a deep dive newsletter, or a highly technical podcast. It allows you to completely bypass the traditional procurement layers of human resources and middle management.

00:07:04 Speaker 1: It just cuts right through it.

00:07:05 Speaker 2: It grants you immediate peer level access to decision makers. They are coming on the show to borrow your audience, and in exchange, you get an hour of undivided attention from someone who normally charges like five thousand dollars an hour for consulting.

00:07:19 Speaker 1: It effectively flattens the entire corporate hierarchy. Which brings us to the massive inflection point in May of twenty twenty six. Because the network, the audience, and the reps she put in didn't just give her a nice Rolodex.

00:07:33 Speaker 2: No, it gave her leverage.

00:07:34 Speaker 1: It gave her the structural confidence to walk into her manager's office at Google and officially resign. She traded that premium total comp package for a modest founder's stipend to co-found a new startup called bounty. With her podcast partner.

00:07:49 Speaker 2: And bounty is operating in a very specific, highly competitive lane right now, the Agentic AI space, right? But they aren't just building another rapper on top of a foundational model, they are positioning bounty as an outcome based AI marketplace.

00:08:04 Speaker 1: Okay, wait, let me hit the brakes and push back on this a bit because I know what everyone listening is thinking right now.

00:08:08 Speaker 2: Let's hear.

00:08:09 Speaker 1: It. Building a YouTube audience of one hundred thousand people is an incredible feat of content creation, but it does not mean enterprise CTOs are going to trust a twenty three year olds Pre-revenue AI startup with their core operations.

00:08:23 Speaker 2: That's a fair concern.

00:08:24 Speaker 1: The market is absolutely saturated with AI startups promising the moon and the failure rate is astronomically high. And according to the article, bounty is pre-revenue and pre-launch. The podcast isn't even generating direct income either. They're purely hunting for corporate sponsorships right now.

00:08:41 Speaker 2: So it's a massive financial risk.

00:08:42 Speaker 1: Yeah. Wait, she traded a guaranteed premium Google salary. We're talking giving up half a million dollars over the next few years for a pre-revenue stipend, relying on a business model where you only get paid if the AI works perfectly. Is that incredibly brave or just naive? Like, how does the actual mechanism of bounty justify that leap?

00:09:03 Speaker 2: It's a great question. If we connect this to the bigger picture of enterprise software, the leap actually makes profound strategic sense because bounty is attacking the single biggest vulnerability in the current tech stack right now, which is subscription fatigue. For the last ten years, the software as a service model has forced companies to pay flat recurring fees for seat licenses.

00:09:23 Speaker 1: For software bloat.

00:09:24 Speaker 2: Exactly. You pay Salesforce or Zendesk, whether your employees actually close tickets or not, you are paying for the capacity to do work, not the work itself, right? Agentic AI changes that paradigm entirely, because these are AI agents designed to execute multi-step corporate workflows completely independently. But the genius of bounty isn't just the AI, it's the pricing mechanism.

00:09:48 Speaker 1: They only charge the client when the software delivers verified real world results.

00:09:53 Speaker 2: Precisely, the client deploys the AI agent into their system, and the financial transaction is triggered only by a verified state change. Like what? Like, did the AI successfully resolve the customer refund ticket and stripe? Did it successfully triage and close the Jira epic? If yes, the microtransaction clears.

00:10:11 Speaker 3: If it fails.

00:10:12 Speaker 2: If the AI gets confused or fails the task, the client pays absolutely zero.

00:10:17 Speaker 1: Wow. Which fundamentally rewrites the enterprise procurement process completely. Because if you are selling a traditional SaaS product, you have to convince the CTO to take on financial risk for a twelve month contract before they even know if the tool works at scale.

00:10:30 Speaker 2: It's a massive barrier to entry for young startup.

00:10:33 Speaker 1: But by moving to an outcome based model, asana eliminates that adoption friction. She is basically telling the enterprise, don't trust my marketing, just plug the API in. If my agent doesn't execute the workflow perfectly, it costs you nothing. It forces the technology to carry one hundred percent of the risk.

00:10:51 Speaker 2: It's a brilliant systemic critique of the bloated software industry. She is aligning her startup's revenue entirely with the client's operational success. But you know, you are right to highlight the psychological chasm between designing a smart business model on a whiteboard and actually giving up your Google badge to go build it, right?

00:11:09 Speaker 3: Those are two very different things.

00:11:11 Speaker 1: And the sauce actually gives us a direct window into that psychology. When asked about leaving, she said, and I'll just quote this directly leaving Google was a risk. But I've always believed that if you feel a strong enough pull toward something, you have to be willing to walk away from good in pursuit of something that could be great.

00:11:29 Speaker 2: That concept, walking away from good for the potential of great is an incredible challenge for highly capable people. It's terrifying.

00:11:36 Speaker 3: It is.

00:11:37 Speaker 1: In computer science, it's known as the local maximum problem. Imagine a graph with hills and valleys. You climb the nearest hill and you reach the top. You look around and you are higher than everything immediately next to you. You've reached a local maximum.

00:11:53 Speaker 2: Okay, so Google was her local maxima.

00:11:56 Speaker 1: Exactly. It pays incredibly well. The status is high, the perks are great.

00:12:00 Speaker 2: But the algorithm stops running because any step you take from the top of that hill goes down.

00:12:04 Speaker 1: Right? You have to be willing to descend into the valley, take a massive short term loss in altitude to traverse the landscape and find the global maximum, the actual highest peak on the map.

00:12:15 Speaker 2: And the human brain is hardwired to fiercely protect the local maximum. And we despise loss of status or security.

00:12:23 Speaker 1: We cling.

00:12:23 Speaker 2: To it. She expands on this, too, stating financial security is comfortable, but it can also be a trap. The scarier version of this decision wasn't leaving Google, it was staying and always wondering what could have been.

00:12:35 Speaker 1: Yeah.

00:12:36 Speaker 2: So what does this all mean for the people listening to this deep dive right now? It really forces a reexamination of how we audit our own careers. It really does.

00:12:44 Speaker 1: We're trained to view a high paying, secure job as the finish line. Ashna views it as a walled garden. A very luxurious walled garden, sure, but one that restricts your root access to the actual open market. It begs the question of whether the quote unquote good situation you are currently fiercely protecting is actually a velvet handcuff, secretly blocking a great one.

00:13:07 Speaker 2: But we also need to critically examine the sequence of her actions here to ensure we aren't just romanticizing the idea of quitting your job.

00:13:15 Speaker 1: Oh for sure. Don't just quit tomorrow, right?

00:13:17 Speaker 2: This wasn't an impulsive leak into the abyss. It was a staged, methodical de-risking process. She didn't quit Google the day she thought of bounty.

00:13:25 Speaker 1: She spent a year building zero to one.

00:13:28 Speaker 2: Yes. She used the podcast to beta test her ability to generate market value from scratch. She proved to herself she could acquire users, secure high level meetings, and synthesize complex information. She gathered hard data on her own competence.

00:13:41 Speaker 1: She basically built a safety net out of distribution and relationships before she cut the financial safety net of her salary.

00:13:47 Speaker 2: Exactly. When most people run a risk assessment on a career pivot, they only calculate the visible cost of failure. You know, if my pre-revenue startup crashes, I lose my Google salary and my Unvested stock.

00:14:00 Speaker 1: That math is very easy to do on a spreadsheet.

00:14:02 Speaker 2: It is. But what I did was rigorously calculate the hidden cost of inaction, the psychological decay of maintaining the status quo, the compounding interest of regret. Wow. She weighed the temporary pain of a potential startup failure against the permanent pain of what if, and realized staying was actually the riskier move for her long term trajectory.

00:14:25 Speaker 1: Which is the exact same muscle she flexed back in twenty twenty four when she demanded the New York office just applied to a much more complex system the demand for a verified outcome. She applied it to her geography. She applied it to her networking via the podcast, and now she is hard coded that exact philosophy into the architecture of bounty.

00:14:42 Speaker 2: Which brings us to the macro implications of that architecture.

00:14:45 Speaker 1: Where does this lead?

00:14:46 Speaker 2: This raises an important question, one that extends far beyond enterprise software sales. Bounty is aggressively shifting the AI software model from guaranteed flat fees for access to getting paid only for verified real world execution, right? It is stripping away the bloat and forcing the system to prove its daily worth. So if the most cutting edge tech companies in the world are currently retraining the enterprise market to only pay for verified outcomes, how long until human employment models follow suit?

00:15:18 Speaker 1: Oh wow.

00:15:18 Speaker 2: We are entirely accustomed to the salaried model. You know, you get paid a flat fee for your time, regardless of whether you shipped a critical product feature or just sat in six hours of pointless meetings. So true. But if the market normalizes outcome based compensation for AI agents, the benchmark for human capital will inevitably adapt. If your paycheck was suddenly tied exclusively to your verified real world results rather than your job title, would you survive or would you thrive? Are you ready for an outcome based career.

00:15:45 Speaker 1: That is the ultimate zero to one test? Thanks for joining us on this deep dive. Keep asking hard questions. Keep looking for the hidden leverage in your own career, and we'll catch you next time.