One rural says it all: “Road grooming ran past our driveway and nobody told us when. We found out when we heard the plow.” That line sits on the LocalXP homepage, and if you’ve ever lived somewhere small you already know how this feels. The closure, the detour, the farmers market that moved: the information existed, it just lived somewhere in a Facebook group, in a thread you weren’t in.

Tom Pischel lives in that reality. Erin is a town of 11,981 people in Wellington County, rural, about 80 kilometres northwest of Toronto and close enough to the Kitchener-Waterloo tech corridor to feel the pull of it.

When a December storm hit, Pischel watched his neighbours coordinate across scattered posts and group chats, and he decided the scatter itself was the problem. As he told the Wellington Advertiser in January 2026, important local information “is often scattered and difficult to follow in real time.”

Most founders would file that under “annoying” and move on. Pischel filed it under “build.”

The web designer who kept building for his neighbours

Watch this founder for the lesson in his track record: LocalXP is his third swing at this problem, and each one got sharper.

Tom Pischel is a front-end designer with about 6 years of product experience. His first project, Dwellcome was an app that helped newcomers to Canada find community resources like halal butchers and daycares.

Then in May 2024 he launched AltGrocery, a free map of thousands of independent food businesses across Canada: farmers markets, local grocers, distilleries. He built it in two weeks after watching the “Loblaws Is Out Of Control” boycott flood Reddit and noticing there were, in his words, “no directories for those looking for alternatives to well-known grocery chains.” He kept it completely free, both for the businesses listed and the people using it.

Notice the through-line:

Same founder, same instinct, 3 angles on one problem: community information is real, valuable, and badly organized.

AltGrocery even lives on inside LocalXP now, as one of the example map layers in the product. Tom absorbed the earlier work rather than abandoning it.

And that is the true signature of a founder who keeps returning to the same problem because he can’t ignore it.

The pivot from points to notice

The most instructive part of the LocalXP story is what changed between that January write-up and the product today.

The early version leaned hard into gamification. You’d earn points for visiting a business, attending an event, or helping a neighbour, redeemable for rewards like a free coffee.

Fun, sticky, and the kind of thing a designer loves to build.

But somewhere between the storm and the current site, Pischel demoted it. The homepage now says: “XP is a single feature, not the platform,” and verified check-ins are “not the default experience.”

What moved to the center instead? Notice. Scheduled closures, grooming windows, market days, trail status, detours. The stuff residents need before they leave the house, rather than the stuff that makes an app feel like a game.

That kind of restraint is rare, and it’s worth crediting. Pischel had a feature he clearly loved, the points and the rewards, and the maturity to set it aside the moment his community showed him what they really needed.

He shipped, he listened, and he let real use rewrite his plan. What the town wanted was simpler than a game: to be told what’s happening on its own streets.

Pischel had the clarity to hear that over his own clever idea, and the confidence to rebuild around it. Choosing the quieter, harder job over the fun one is the instinct that separates founders who serve a community from founders who just launch at it.

Why the small town is the moat

The instinct in startup land is to build for the biggest market you can describe on a slide. Pischel did the opposite, and it works in his favour.

Living in Erin means his feedback loop is literally at his front door. The municipal clerk who says “we post closures on Facebook, half the town never sees them” could be someone down the road from him. He can watch a program run, see what confuses people, and fix it by the weekend.

It also gives him a wedge the giants ignore. A town of 12,000 is too small for enterprise GIS pricing and too real to be served by a generic Facebook group. LocalXP is explicitly built to be “affordable for small communities and BIAs, not enterprise GIS pricing,” and it’s designed so a municipality, a utility, a business district, and a tourism group can each run their own layer on one shared map.

The design carries the same local logic. On LocalXP, one business keeps a single profile that can surface on the downtown map, the weekend market, a festival overlay, and a trail program at once, instead of rebuilding four separate listings. A bakery updates its hours once and stays current everywhere.

For a small main-street shop with no marketing budget, that’s the difference between showing up and staying invisible, and it’s the kind of unglamorous work you only think to build when the shop owners are people you actually know.

Bootstrapping this company also keeps him honest. Pischel funds LocalXP with rental income and paused his paid design work to build it, so there’s no investor clock pushing him to overstate the market or rush a launch. He can afford to get it right for one community before offering it to a hundred, and that patience is its own advantage.

The gap Tom is building into

The market case here rests on a documented collapse. Between 2008 and October 2025, 603 local news outlets closed across 388 communities in Canada, and only 264 new ones launched and survived. The local newspaper that used to run the closures, the market times, and the road work is, in hundreds of towns, simply gone.

Something has to hold that civic information, and right now the answer is “a dozen Facebook groups and a PDF.” That’s the vacuum LocalXP is walking into.

Rural towns like Erin sit right in the blast radius of that decline. When the shared source of “what’s happening here” disappears, the town doesn’t stop needing one. It just makes do with worse until someone builds a better one. Pischel is betting the better one is a map the community can trust and, crucially, own.

What this means for your startup

You don’t need a rural town or a mapping product to use what Pischel is doing. The moves are portable.

Start closer than you think

Tom’s whole approach fits in one sentence: live where the problem lives, ship to your neighbours, and let their real needs shape what you build. LocalXP is still early, with guided pilots running now and subscriptions opening soon, and whatever comes next, the way he got here is a playbook any founder can run. He turned a place most founders would drive right past into the one advantage nobody can take from him.

The best market research is a life lived close to the problem. You can’t outspend that, and you don’t have to. You just have to start where you already are.

About the Author

Lidia Vijga

Lidia Vijga is a content engineer focused on building in-house growth engines and training AI agents that help companies scale content and dominate brand visibility across traditional and AI search. She is also co-founder of DeckLinks, bringing firsthand founder experience in building and scaling technology products.

The post Why a Solo Founder Bet on His Town Instead of the Big City appeared first on BYVI.

Maor Shlomo built Base44 by himself, went from $0 to $1 million ARR in 3 weeks, grew it past 400,000 users, and sold it to Wix for $80 million about 6 months in, without raising a dollar. No co-founder, fewer than 10 people, sole shareholder. That’s the screenshot every founder saved this year, and the proof text for a genuinely huge shift: solo founders now make up 63% of the C corps formed through Stripe Atlas in Q2 2026, an all-time high.

That same Stripe post carries a second, quieter number. The typical solo founder, the one right in the middle, made 23% less in their first 6 months than the typical founder a year earlier.

The top 10% made 19% more. So the money is splitting: the average founder is sliding while the best pull away, and the gap between them is the widest it’s ever been.

In 2022, the top solo founders made 34 times as much as the typical one in their first 6 months. By 2025 they made 61 times as much. The boom is real. So is the fact that most of the people inside it are earning less than solo founders did a year ago.

The number everyone screenshotted hides a second number

Most people share the 63% as a win for solo founders. Flip it around and it reads as a warning. If solo founders are 63% of new companies and the typical one is earning less, that’s not more opportunity to go around. It’s more founders fighting over the same customers: more people building, the same number of buyers, smaller slices for everyone.

The good news and the warning are the same fact seen from two sides. When anyone can ship software in a weekend, “I built a product on my own” stops being impressive the moment 100,000 other people did it this quarter too. Building used to be rare enough to set you apart. Now it doesn’t.

When everyone is AI-native, AI-native stops being an edge

Stripe found top-decile solo founders were about twice as likely as median founders to build AI-native products, and the obvious takeaway is “go build AI-native.” Except the same analysis notes the AI-native advantage doesn’t come from a few breakout apps at the very top. It shows up across the whole distribution, from roughly the 50th to the 95th percentile. AI-native is table stakes now, not an edge.

Think about what that means for the weekend build. The tool that let you ship a working product in 2 days is the same tool a hundred thousand other founders used the same weekend.

The build was never going to be the moat. Marc Lou, who’s launched 35 startups solo, says: the next generation “will be no-code people focused on solving a problem, shipping crazy fast with AI, and cracking distribution on social media.” The moat moved. It sits in two places now, and the best founders grab both.

Speed wins the market

Base44 is a speed story before it’s anything else. Shlomo didn’t out-engineer the field. He got a rough product in front of people, found what worked, and captured a category in weeks: $0 to $1 million ARR in 3 weeks, 140,000 users in 7. By the time competitors had a serious answer, he’d sold the lead to Wix for $80 million.

When building is free, the first founder to reach real customers at scale gets a head start everyone else has to spend money to erase.

That head start is worth more in 2026 than it’s ever been, because it’s more perishable than ever. The clone that used to take a competitor 6 months now takes a weekend, so the window where being first actually means something is short.

You either use that window to grab share, sign customers, and become the default answer, or you lose it fine-tuning a product nobody’s tried yet

Shlomo used that head start to grab the market, then took the exit before the moat question even came due. Speed didn’t just win him the market. It won him the option to leave on top.

Brand keeps it

Speed opens the lead. It doesn’t hold it, and most founders aren’t exiting in 6 months. Something has to make customers stay when a cheaper copy shows up next Tuesday, and the most reliable something is a brand a competitor can’t fork.

Pieter Levels is that lesson stretched over a decade. He runs a portfolio that publicly reports around $3 million a year with zero employees, on a famously boring stack of PHP, jQuery, and SQLite.

What isn’t boring is the audience of roughly 600,000 he built over 10 years of shipping in public. When he launched Photo AI, it did a few thousand dollars in week one and grew to six figures in monthly revenue inside 18 months. The code was ordinary. The people who already trusted him were the whole engine, and they’re the reason a hundred identical AI photo apps never took his lunch.

Speed and brand aren’t rival strategies. They’re two halves of the same job. Speed wins you the market before the crowd arrives. Brand keeps you there once it does. Base44 leaned on speed and sold fast. Pieter Levels leaned on brand and keeps growing year after year. Most founders who want to still be here in 3 years will need both.

The playbook I’d run right now

The good news buried in Stripe’s data is that the winners aren’t winning on pure luck. They share habits you can copy today, and none of them require a bigger team.

Win it fast, then make it stick

The boom didn’t do the hard part for you, and that’s the assumption founders keep tripping over. When building gets cheap, your edge has to come from what’s still hard: getting there first, grabbing the market, and earning the trust that keeps it.

And that’s the good news. Effort still wins, and the founders willing to put it in have a bigger edge than ever.

About the author

Lidia Vijga

Lidia Vijga is a content engineer focused on building in-house growth engines and training AI agents that help companies scale content and dominate brand visibility across traditional and AI search. She is also co-founder of DeckLinks, bringing firsthand founder experience in building and scaling technology products.

The post Everyone Can Build Now: Speed Wins the Market and Brand Keeps It appeared first on BYVI.

I flew to San Francisco for YC Startup School 2026 with something I didn’t have a year ago: a product real people actually use. Having a real product changed everything about this trip.

Here’s what the sessions, the hallway run-ins, and 6,000 founders in one place did to the way I think about building, and why I came home to Ottawa readier to go bigger on my own terms.

Why I live on Twitter/X, not LinkedIn

Two lines from the stage stuck with me before I’d even left the building. Boris Cherny joked, “Maybe don’t listen to the LinkedIn influencers.”

Alexandr Wang’s session put it plainer: “Top advice: ignore LinkedIn.” My takeaway landed fast: more Twitter/X, less LinkedIn.

Strange thing to write in a piece you might be reading on LinkedIn, I know. But it’s the honest version.

The reason I’ve been able to track AI closely and pick up new tools quickly is Twitter/X. It’s the platform I’ve used longest and most consistently, and it’s where people share what they’re trying before it gets sanded into polished content. I spot a tool early, then I find out if it’s any good by throwing it at a problem I already have.

The room didn’t make me a builder

The bigger takeaway from the trip, though, was confidence. And that’s really what I want to hand to anyone reading this who doesn’t have the “right” background and is wondering whether to build anyway.

Startup School didn’t turn me into a builder. Building had already changed who I was by the time I showed up. Over the past year, AI stretched what I could do alone. The people I met stretched what I thought was worth doing.

What my YC Startup School experience actually gave me

Last year I applied to Startup School and got rejected. I flew to the Bay Area anyway, went to the events around it, and found my way into the community.

That trip forced me to face something: I had years of product experience and pages of ideas, but nothing I could point at and say, ‘I made this.’

One year later, I came back with Joe Speaking. It started as a fix for my own English-speaking problem. I was user number one, and it got me the language score I needed. Since then it’s reached more than 1,700 users through purely organic growth, 130 of them paying, and it became my first iOS app.

At Startup School I met people from top universities, research labs, and big tech. Plenty were younger than me with resumes that read better than mine on paper. A year earlier, that would’ve had me quietly questioning whether I belonged. This time I just felt proud. I didn’t need a famous school, company, title, or investor to explain who I was, because I’d put something real into the world and people were using it.

Building became my credential.

Startup School didn’t give me that. It helped me see I’d already earned it. While I was drafting this, Paul Graham posted the line that sums it up: confidence comes from evidence.

Here’s my evidence from one year. I went from product person to someone who can turn a personal problem into a shipped product, then debug it, distribute it, support it, and improve it.

Even while wandering around San Francisco, I shipped 1 iOS release and 3 web releases remotely, acted on feedback from 3 users, and burned through roughly 4.4 billion tokens. That pace still feels a little unreal to me.

There’s no magic prompt

Boris Cherny’s session explained why some people get so much more out of tools like Claude Code. There’s no magic prompt. His advice was to treat the model empirically: give it hard work, give it a way to check its own output, watch where it breaks, then adjust the prompt, context, tools, or workflow. Then run it again. The loop is the skill, not any single line you paste in.

That’s exactly how I got better. Not by hoarding prompt templates, but by testing tools against problems I genuinely needed to solve and letting the results grade me. A template promises a shortcut, but the model, the task, and the tools keep shifting, so what worked last week quietly stops working.

Running that loop against my own real problems taught me what to actually change when the output missed, and the feedback was honest because it was mine.

AI didn’t take my job off my plate. I still had to decide what to build, understand users, judge the output, and make the tradeoffs. What it did was amplify my agency.

One person still isn’t enough

The trip reminded me that one person still isn’t enough for everything. Tools made me faster. The people I met changed what I thought was possible, and brought the judgment, trust, and perspective no tool can provide.

Permission to build what you actually want

Most conversations gravitated to giant markets, hockey-stick growth, fundraising, and whatever’s trending. That energy is part of what makes the place worth it. My path just looks different.

Joe Speaking is a product, an experiment, and a way to learn all at once. I built it because I needed it, not because the problem was fashionable or easy to pitch. I literally put my own name on it.

Because AI has dropped the cost of execution, I have more room to follow that instinct. But more freedom raises the stakes on choosing a direction.

AI can make me faster. It can’t tell me what to care about.

What I took from Peter Steinberger’s talk was permission:

Afterward I caught a few minutes with a YC partner, walked through my background, why I built Joe Speaking, and what I want to explore next. The product has grown further than I expected and become genuinely useful to a group of people. But I’m not going to jam it into a story built for YC or fundraising before I understand what the bigger company should even be. I might apply later, when the product, the opportunity, and my ambition actually line up. For now I’m fine saying: not yet.

That’s probably the cleanest summary of my trip. Call it a refusal to perform ambition for other people. I’m plenty ambitious. I just won’t fake a version of it to fit someone’s slide.

Building for myself means starting from a problem I understand deeply, listening to users, and seeing how far it goes before I call it something bigger. Feeling a little out of place might’ve been part of finally finding my own lane.

> be joe.

> always build for myself and be my own first user. even the product name has “joe” in it.

> build to solve my own problems, not to optimize for yc or fundraising.

> twitter is still on my phone dock.

> on my own track.

Thank you @steipete ! pic.twitter.com/octq3iwkQq

— Joe Hu (@hubeiqiao) July 26, 2026

Staying on your track doesn’t mean thinking small

Authenticity can turn into an excuse, though. “I’m staying on my own track” should never quietly mean “I’m scared to think bigger.” Startup School pushed me here, hard.

Alexandr Wang framed this as a once-in-a-civilization moment. The question I walked out with: what kind of world do I want my work to help build? I’m good at spotting a concrete problem and shipping a practical fix. I’m still learning to connect those fixes to a much larger vision.

Sam Altman argued that coding agents have raised the ceiling for small teams. They let a tiny team chase work that used to be out of reach, not just do the familiar stuff faster.

Blake Scholl made that tangible in the physical world. He came from software, not aerospace, and still built toward an economically viable supersonic airliner. His story was a reminder that better technology doesn’t hand us a better future on its own. Someone has to build the products that carry it forward. I’ve long been drawn to products that bridge the physical and digital worlds. I don’t know the exact shape yet, but Startup School pushed me to look past whatever’s directly in front of me.

Later, at a fireside with Vinod Khosla, it got even blunter: dream bigger, take more risks, accept that you might fail. For me that means pushing my own path further than I first thought possible, instead of copying someone else’s company or someone else’s definition of winning.

“How hard can it be?”

Jensen Huang’s session gave me another handle on confidence. NVIDIA didn’t start with the right technical approach. When the team finally admitted that, they bought textbooks, learned what they had to, and rebuilt. His point was simple:

Great founders don’t already know everything. They’re willing to face reality and go learn.

He described the attitude he still brings to unfamiliar problems: “How hard can it be?” The honest answer is usually “harder than you think.” But picturing every future ordeal in advance can rattle you enough to never start. So start. Learn what today asks of you. Then build toward tomorrow.

And that felt personal. Last year I didn’t know how to build the product I now run. This year, users occasionally wake me up with support messages. New problems, sure, but problems I’m grateful to have. Resilience isn’t repeating the same idea forever or pretending everything works. It’s facing what’s real, learning fast, adjusting, and continuing. Not knowing something isn’t a permanent identity. You can learn your way there.

The best of the YC Startup School experience happened between sessions

The talks were great. Some of the meaningful stuff happened in the gaps. At a Google DeepMind after-party I met Logan Kilpatrick and showed him what I’d built with Gemini Live. Telling the builder what I thought, face to face, beat any feedback form I’ve ever filled out.

I first met Nolan Lwin at Founders, Inc. We crossed paths again at Startup School, then at a coffee shop he recommended, where nearly everyone around me was grinding on a YC application. Everyone but me.

Coming back from the restroom, someone stopped me: Mark Nakatani. We’d tried to meet at Startup School and missed each other, only to collide there by accident. I also finally met Nelson Lee in person after knowing him through X for years.

There were real conversations, new friendships, after-parties, and even builder hotpot. Some of these meetings probably felt ordinary to the other person. To me they carried years of context.

When you’ve followed someone’s work, used what they built, or traded posts for a long time, meeting in person changes the whole thing.

Help first, the network builds itself

Sam Altman also talked about how the best networks grow from genuine curiosity and helping people, not from treating every chat as a transaction. That matched my whole trip. I didn’t walk into rooms hunting a target. I shared what I was building, listened to other people’s stories, asked questions, and let the unexpected conversations happen.

It’s how I think about a personal brand too. To me, a brand is the memory people build from what you do over and over, not a polished profile or a job title.

Sometimes helping is sharing what you know or making an intro. Sometimes it’s honest feedback, or just caring about what someone’s trying to build. Writing this is one of those ways.

I’m early, and I haven’t figured it all out. But maybe the honest version of my journey helps someone start sooner, feel less intimidated, or trust their own path a little more.

I want what I publish to make me more useful and more honest, not just more visible.

What San Francisco gives me, and what Ottawa does

In San Francisco, especially at a YC event, ambitious ideas feel normal. Conversations open with agents, models, infrastructure, distribution, and products that were impossible a year ago. Startup thinking leaks into ordinary places. That density is powerful, and it’s still a bubble. Step outside it and plenty of people have never meaningfully used an AI agent, and plenty of businesses are only starting to grasp what today’s tools can do.

There’s an insane amount of alpha in:
– People in SF leaving SF to see how people and companies around the world are using AI (hint: most are still using it primarily as search)
– People outside SF spending even two weeks in SF to see how people and companies are operating here…

— Akshay Kothari (@akothari) July 21, 2026

So the right rhythm for me might be to visit San Francisco now and then, soak up the ambition, meet people, then go home to communities where the problems and the level of AI adoption look completely different.

I sometimes wonder what would’ve happened if I’d moved straight to the Bay instead of staying in Ottawa. Maybe I’d be more connected, or building faster. Maybe I’d have optimized too quickly for the conversations right around me. Ottawa gave me distance, and time to explore freely and find my own direction. That distance cost me access. It also gave me room to become different. San Francisco shows me what’s possible. Ottawa reminds me how much of the world hasn’t reached that future yet.

I also met a lot of Canadians on this trip, a surprising number of them from Waterloo. It left me chewing on Canada’s talent story. How do we create more places where ambitious builders keep bumping into each other? How do we give them better reasons to take risks and stay long enough to build the next wave of communities?

I don’t have a tidy policy answer. But as an immigrant building here, I want Canada to be more than a place that grows and attracts talent right before that talent leaves. I dig into that more here: Canada helped Joe become a builder. Does Canada know how to keep builders?

What I’m taking home from the YC Startup School experience

Last year I flew to the Bay with ideas and uncertainty. This year I flew home with a product, real users, more confidence, and friends from around the world. Startup School felt like a reward for the previous year’s work, and a reminder that a personal win doesn’t have to follow anyone else’s template.

You don’t have to chase every trend or reshape yourself for investors and algorithms. Start with something you actually care about, keep learning, help people, and build something you’re proud to share.

I still don’t know exactly what I’ll build next, or who I’ll be when I’m back in San Francisco. But I know how I want to keep going:

• Be real.
• Be yourself.
• Help others genuinely.
• Think bigger.
• Stay on your own track.

Thank you, Y Combinator, for putting this many ambitious, thoughtful, and generous people in one place.

About the Author

Joe Hu is the founder of Just Joe Technologies, a one-person, AI-powered studio based in Ottawa, Canada, where he builds products with AI and shares the journey publicly. His first iOS app, Joe Speaking, started as a fix for his own English-speaking problem. He writes about building as an explorer, builder, and founder at hubeiqiao.com and posts on X @hubeiqiao.

The post How YC Startup School Rewired the Way I Build appeared first on BYVI.

The hardest part of building something new is never the part everyone assumes will be hard. I keep coming back to this, and Travis Kalanick’s new venture is the cleanest example I’ve seen in a while.

After roughly 8 years of near-total stealth following his 2017 exit from Uber, Kalanick’s company Atoms just raised $1.7 billion in a round led by Andreessen Horowitz, with Uber (the company whose investors forced him out) joining as an investor and Ben Horowitz taking a board seat.

The pitch is enormous: use software, sensors, and robotics to automate the physical economy across food, mining, and logistics

Travis Kalanick is back.

After 8 years of building in stealth, he joins Ben Horowitz and Erik Torenberg to tell the story of his latest company, Atoms, and its vision to digitize the physical world one industry at a time:

01:32 The a16z & Uber backstory
15:36 Different… https://t.co/CmHLuzWYBR pic.twitter.com/hjzHk1vkBf

— a16z (@a16z) July 22, 2026

Ask most people what the hard part of an automated kitchen is, and you’ll hear the same list:

• The computer vision.
• The robotic arms.
• The supply chain.
• The capital.

Every one of these sounds hard because it is hard, and because it’s what makes it into the pitch deck. So that’s where the money and the engineering attention go.

The hard part turned out to be everything nobody puts on a slide.

The machine that slows down when it has to bag your food

Atoms’ flagship food system, the Bowl Builder, takes an order straight off the point-of-sale, dispenses and weighs ingredients, adds sauce, lids the bowl, labels it, drops in utensils, and closes the bag. On paper it can assemble up to 300 bowls per hour.

Except it can’t, not when it also has to bag them. The moment bagging enters the workflow, throughput drops to about 200 bowls per hour. Kalanick’s own word for the bagging problem, on the TBPN podcast, was that it’s “surprisingly hard.”

Look at what that means. A company that just pulled $1.7 billion in a single round, run by one of the most operationally aggressive founders of his generation, loses a third of its output to the most unexpected step in the whole process. And it’s not the sensing. Not the dispensing. Not even the sauce. It’s the bag.

That’s the “fries in the bag” problem: the step so ordinary nobody budgets it any respect, right up until it’s the exact thing standing between you and shipping.

Why the boring problems are the hard ones

There’s a reason this pattern repeats. The glamorous problems attract attention, talent, and money precisely because everyone can see them coming. They get scoped, staffed, and de-risked. The boring problems don’t get that treatment, so they arrive undefended, and they arrive late, usually after you’ve already committed to a timeline and a number.

A bowl is a rigid, predictable object. A plastic bag is not. It’s floppy, it clings to itself, it doesn’t hold its shape, and no two are quite alike. Everything a robot is good at (precision, repetition, hard geometry) is exactly what the bag refuses to give it.

This is what quietly reshaped how I think about leadership, and about finance specifically.

The leadership lesson hiding in the bag

It’s tempting to treat “hard problems” as the big strategic ones, the market, the model, the fundraise, and hand the messy operational stuff to someone else. That instinct feels like leverage. It’s usually the opposite.

The leaders who actually ship are the ones who stayed close enough to the work to know where the real friction is.

Not because they insist on doing everything themselves; that doesn’t scale and it isn’t the point. It’s that you cannot fix what you’ve never gotten close enough to see. Kalanick knows his throughput drops on the bagging step because someone in that organization got close enough to the machine to watch it happen and put a number on it.

Delegation doesn’t work against this. Good delegation is how you build a company. But hand off a problem before you understand it and all you’ve really done is arrange for someone else to discover it for you, later, and usually at far more cost to the business than if you’d caught it early.

I sit in finance, which makes this personal in a specific way. Every model I’ve ever built has a “fries in the bag” line item: the assumption everyone waved through because it seemed too small to matter. The onboarding step that quietly determines whether a customer ever activates. The reconciliation process that works fine at 50 transactions and breaks at 5,000. The manual handoff between two teams that no dashboard captures because it lives in the gap between them.

On a spreadsheet these look like rounding errors. In the business they’re the difference between a plan that holds and one that doesn’t.

The costliest surprises I’ve seen were never the risks we named and argued about in the boardroom. We had those covered; that’s what boardrooms are for. The expensive ones were the steps so ordinary nobody thought to ask whether they’d actually work at scale.

What Atoms actually confirms

It would be easy to read the Atoms story as a victory lap: stealth genius resurfaces, raises a fortune, takes on the physical world. And maybe it turns into that. Horowitz is betting that reshaping the old, heavy parts of the economy needs a founder with range across software and mechanical engineering, and that Kalanick is that founder.

But the part of the story I keep pointing people to is the honesty about the bag, not the $1.7 billion. A founder with that much capital and conviction telling you, plainly, that the humble step is the one giving him trouble. Read it the right way and that’s a good sign: someone got close enough to the friction to name it before it showed up in the numbers on its own.

The strategic bets are real, and they’ve earned the attention they get. But the thing most likely to cost you a third of your output is usually sitting one step downstream of where everyone is looking, dressed up as a task too simple to fail.

Before I sign off on any plan now, I ask myself one thing, and I’d pass it to you:

What’s your “fries in the bag” problem, the boring step you’ve already handed off, assuming it would sort itself out?

Better to go find it yourself than to read about it later in the P&L.

About the Author

Daniel Zhao is Head of Finance at Landbase and a multiple-time founder, most recently co-founder of LavaReach (acquired). He started his career as a CPA, building the financial rigor that still shapes how he operates, then moved from the ledger into the parts of a company where the numbers are actually made or lost: revenue, operations, and go-to-market. That path, from accounting fundamentals to B2B revenue leadership, gives him a rare vantage point on how startups really grow, and where they quietly break.

The post Every Startup Has a “Fries in the Bag” Problem Most Founders Miss appeared first on BYVI.

A shopper opens ChatGPT and types “best standing desk under $400 that ships in two days.” Thirty seconds later they have 3 recommendations, a comparison, and a checkout button. They never visited a search results page. They never saw your ad. The agent already decided, and if your product wasn’t in its consideration set, you lost a sale you never knew existed.

This is agentic commerce, and in 2026 it stopped being just a demo. AI agents are completing real purchases inside ChatGPT, Perplexity, Google’s AI Mode, Microsoft Copilot and Amazon Rufus. AI platforms are projected to drive roughly $20.9 billion in retail spending this year, nearly quadrupling 2025.

The funnel is collapsing into a single conversation, and the new question for founders is brutally simple:

The part that’s good news for small teams is that agents don’t reward ad budgets. What they do reward is clean data, trust signals, and being present in the right feeds.

First, understand how an agent actually picks

AI shopping agents don’t “rank” pages the way Google’s blue links did. They assign confidence scores, every product gets scored on how well it matches the shopper’s stated criteria, and the agent recommends what it can stand behind.

To score you confidently, the agent needs 3 things:

1. Structured data it can easily read
2. Trust signals that prove you’re legitimate
3. Validation from sources it already trusts

Your Google Merchant Center feed and your organic SEO are still load-bearing. AI commerce is a new layer on top, not a replacement.

Step 1: Get into the feeds, all of them

You can’t be recommended if you’re not in the pipe. There are several entry points, and they’re cheaper to set up than most founders assume.

Underneath them, 2 open standards now move the money and the data.

1. The Agentic Commerce Protocol (ACP): co-developed by OpenAI and Stripe, now with 25+ partners — standardizes agentic checkout and powers Instant Checkout in ChatGPT.
2. The Universal Commerce Protocol (UCP): from Shopify and Google, with a Tech Council that now includes Amazon, Meta, Microsoft, Salesforce and Stripe — covers the broader journey from product discovery through post-purchase.

You don’t need to implement either by hand; your platform increasingly does it for you. But knowing they exist tells you where the rails are going.

Step 2: Make your product data machine-perfect

This is where most stores quietly lose. Agents read fields in a specific priority order, and gaps get filled with guesses, or with a competitor who left no gaps. Stores with 99.9% attribute completion are seeing 3–4x higher visibility than stores with sparse data.

Work the fields in order of impact:

Step 3: Use JSON-LD schema on every product page

The feed is one layer; your live product pages are the open-web layer that AI crawlers read directly.

Implement Product, Offer, and Review schema in JSON-LD, it’s the preferred format because it keeps structured data separate from your HTML, so AI parsers extract it cleanly without inference.

This is the difference between an agent reading your specs and an agent guessing them. Validate it, and make sure every product has it. This is a one-time engineering task with compounding payoff.

Step 4: Win on trust signals, reviews are now ranking fuel

Reviews stopped being social proof for humans and became input data for machines. The ChatGPT feed spec explicitly accepts review count and average rating fields, and agents weight them heavily when deciding who to recommend. Amazon Rufus synthesizes review sentiment and Q&A directly. Fresh, plentiful reviews give the LLM the recent, specific evidence it needs to recommend you with confidence.

Tactically: turn on post-purchase review requests, make leaving a review frictionless, and feed your review count and rating into your product feed. A product with 400 reviews at 4.7 stars beats an identical product with 12 reviews every time an agent has to choose.

Step 5: Build third-party citations (this is your Perplexity edge)

Perplexity in particular prioritizes citations from Reddit, expert blogs, and editorial sources over your own marketing copy. Agents trust what other credible sources say about you more than what you say about yourself.

This is where bootstrapped and founder-led brands have an unfair advantage. You can get mentioned in niche communities, founder newsletters, and subreddit threads in ways big brands can’t fake.

Get your product into legitimate “best X for Y” roundups, answer honestly in relevant Reddit threads, earn reviews on independent blogs. Every credible third-party mention is a citation an agent can lean on.

Step 6: Exploit the rich-media gap before everyone else does

The ChatGPT feed spec accepts video and 3D model links, and almost nobody is using them yet. That’s a textbook early-adopter window. Richer media gives the agent more to understand and more confidence to recommend, and right now adding a product video or 3D asset is a near-free way to stand out in a field of plain image feeds.

Step 7: Write conversational content that matches how people actually ask

Agents answer questions, so publish content shaped like the questions buyers ask:

• “Is X good for small apartments?”,
• “What’s the difference between X and Y?”
• “Best X for beginners on a budget?”

Clear, factual, comparison-friendly pages give agents quotable, citable material. Agents reward specificity, not adjectives, so skip all the fluff.

Step 8: Measure what’s working

You finally have instruments. Google launched Search Console’s Search Generative AI performance reports in June 2026 (initially to a limited set of sites) surfacing impressions and pages appearing in AI experiences like AI Overviews and AI Mode. Watch which products show up, test feed changes, and double down on what lifts your presence. Treat it like any other channel: instrument, iterate, compound.

The 10-Minute Checklist to Get Recommended by AI Shopping Agents

Pin this somewhere visible:

• Google Merchant Center feed live and clean
• Shopify Agentic Storefronts (or your platform’s equivalent) enabled
• ChatGPT feed + Perplexity Merchant Program submitted
• Titles loaded with brand + type + feature + differentiator
• GTINs and full attributes on every product (aim for ~100% completion)
• Shipping time and returns as structured, parseable data
• Product + Offer + Review JSON-LD schema on every page
• Review count and rating flowing into the feed; review requests automated
• Third-party citations seeded (Reddit, roundups, expert blogs)
• Video/3D media added where the spec allows
• Feed price = storefront price, inventory synced
• Search Console AI report monitored monthly

The takeaway

Agentic commerce rewards the unglamorous work: complete data, real reviews, clean feeds, honest third-party presence. None of it requires a brand budget, which is exactly why disciplined small teams can out-rank incumbents in the agent’s consideration set.

Over the next few years, the brands that win will be the ones the machine can read most clearly, and that’s the opportunity in front of you. You don’t need a bigger budget, just better data than the competitor sitting one row away in the results.

So start today. Clean the feed, sharpen your titles, turn on reviews. Every field you complete gives the agent another reason to choose you.

The AI agents are already shopping. Make sure they find you first.

About the Author

Assel Beglinova is a CEO and Co-founder at Paperstack, a platform that provides working capital for e-commerce sellers. With her expertise in revenue-based financing, Assel helps merchants achieve their growth goals and thrive in the competitive world of e-commerce.

The post How to Get Your Product Recommended by AI Shopping Agents appeared first on BYVI.

If you are planning a startup journey or already in the middle of one it’s really important to take a step back and think through your personal finances. Startup life can be unpredictable, and not having your finances in order can add a lot of unnecessary stress and affect not just your personal life but also to how you run your company. Having a solid financial plan gives you the peace of mind to focus on building and growing your startup. This is the reason I built Planwell, to help people plan their finances for major life decisions.

Here is the definitive guide for the major financial decisions every founder should think about before and during the early startup phase.

Decide how long you will commit to your startup

It is wise to give yourself a certain period of time to work on your startup and evaluate whether it will work out. Set clear milestones that you need to achieve within a certain period of time.

This time-bound approach creates focus and prevents the startup from becoming a never-ending project. It also helps you plan your finances better.

How does your stage of life affect this decision?

Your financial strategy will almost certainly differ depending on where you are in life. Early career founders often have lower fixed costs and may still be on a parent’s health insurance plan, which makes a shorter runway more manageable. Founders with families face higher fixed costs such as childcare, schooling and family health insurance. However, they may also have a higher level of savings accumulated and potentially a partner whose income or insurance provides a buffer. Take stock of your full picture and try to maintain a higher personal reserve before committing fully. 

Analyze your cash flow and budget requirements

Before leaving your salaried job, analyze your personal cash flow and understand how much you spend every month. In particular, try to figure out the discretionary expenses that you could cut while you are working on the startup. 

What can you do to prepare financially while still in a W2 role?

If you have the leeway, try to keep your fixed costs low. If you have the dream of doing a startup down the line, do not buy the most expensive home and do not max out your lifestyle, even if you can afford it. The flexibility you preserve now becomes the runway you have later.

What are your health insurance options as a founder?

Unless you have a spouse or parent whose health insurance you can rely on, you need to figure out health insurance options and budget for them before you make the leap. You can purchase coverage through the Individual Marketplace via the Affordable Care Act (ACA). Typical monthly premiums vary by age and state:

Early-stage teams sometimes use services like Take Command Health to navigate options.

Estimate whether you have funds to cover your budget

How many months or years of runway do you have to cover your budget? Does this match the period of time you gave yourself to work on your startup and take it to the next level?

This calculation determines whether you can bootstrap or whether you need other income sources.

How can you model your personal financial runway before quitting your job?

Start with the calculation above, but keep in mind that most founders underestimate costs like healthcare, quarterly taxes and irregular expenses.

A tool like Planwell’s Career Break Planner  lets you input your exact income, expenses, assets and goals and stress-test your runway under different spending scenarios. You can also model how a major decision like buying a home changes how long your savings will last. Unlike a generic AI tool like ChatGPT, Planwell uses its own financial models and runs your exact numbers, so the output is specific to your situation rather than a general answer that could apply to anyone.

Build a founder-sized emergency fund

The standard advice, three to six months of expenses in reserve, was written for people with stable salaries. As a founder, aim for at least twelve months of personal expenses in liquid savings. Keep this separate from whatever you have allocated to the business. This will help ensure that an unexpected expense or a delayed funding round does not force a bad business decision.

If You Don’t Have Enough Savings, Consider Your Options

Review legal and tax obligations

When you set up your startup in the US, you have to make sure to file Form 83(b) which lets you pay taxes upfront on the restricted stocks at the value at grant rather than when the stock vests. This is because stocks appreciate upon vesting and you will have to pay ordinary income tax upon vesting. The form 83(b) has to be filed within 30 days of the grant and this is absolute. Missing filing the 83(b) within this 30-day period is usually irreversible and can lead to massive unexpected tax bills. Set a calendar reminder the day you sign your founding documents.

What are your retirement savings options as a self-employed founder?

As much as possible, do not deplete your retirement funds while building your startup. As a self-employed founder you have access to several tax-advantaged vehicles:

Understand your tax position as a self-employed founder

Your tax obligations depend on how your business is structured and how you pay yourself. If you’re operating as a sole proprietor, LLC, or partnership, taxes typically aren’t withheld automatically, and you may need to make quarterly estimated tax payments. Depending on your structure, you may also owe self-employment tax. By contrast, founders of C-Corporations who pay themselves a salary generally receive a W-2, with payroll taxes withheld just like any other employee. As a starting point, many founders set aside 25–35% of income for taxes, although the right amount depends on your income level and state of residence. Before choosing an entity structure, speak with a CPA who works with startups. The decision can have long-term implications for taxes, fundraising, and equity compensation.

Separate personal and company finances

It is wise to get a separate bank account for the business and not mix your personal and professional accounts. Even if you are the one funding the business, having a separate account helps you track how much you are actually spending on the business.

Think Carefully About Bootstrapping

Bootstrapping can be a great way to maintain ownership, but it also concentrates risk. Avoid putting all your personal assets into the company. Instead, allocate a specific budget for bootstrapping. 

Tie that budget to milestones, such as:

Consider part-time or side gig income

Even if you go full-time, you may want to consider part time or contract work on the side if finances are a crunch. You can consider consulting, contract work or freelance projects. This income can extend your personal runway while you are working on your startup.

Plan your founder salary if you have raised funding

Once you have raised funding, the question of how to pay yourself becomes more structured. Here is what founders typically do.

When should you start paying yourself?

Most founders begin paying themselves once the company raises funding or the business generates consistent revenue. The goal is not to match a big tech salary but to cover living expenses so you can focus fully on the company.

How much should you pay yourself?

Founder salaries typically depend on the funding stage and location. Many early-stage founders pay themselves enough to cover basic living expenses, often in the range of $70k to $120k in expensive cities, though this varies widely.

Investors generally expect founders to live comfortably but not extravagantly. Investors do not want you to be anxious and distracted due to money troubles, and encourage founders to take a decent salary that helps support their living expenses but otherwise want you to deploy all your resources into growing the business.

Think about equity as part of your personal finances

Your equity stake may represent the majority of your theoretical net worth on paper. It is also completely illiquid and might end up being worth nothing. Planwell’s lifetime financial projection shows your actual net worth at every stage of life, which is a useful reality check when equity makes your paper wealth look larger than your actual financial position. Plan your personal finances as if equity does not exist: maintain your savings, keep contributing to retirement, manage your fixed costs, and treat equity as optionality rather than income.

Protect your personal credit

Your credit score matters more as a founder than it ever did as an employee. Business credit lines and financing can touch it. Keep personal and business spending cleanly separated, pay personal obligations on time even when business finances are tight, and avoid personal guarantees until you fully understand what you are signing.

Plan around major life events

Buying a home, having a child, supporting an aging parent. These do not pause because you are building a company, and each one can materially change your personal runway overnight. Founders make the mistake of treating these as separate from their startup finances whereas they are deeply connected. Planwell is built for exactly this: you can run what-if scenarios to see how a single major decision like taking on a mortgage ripples through your cash flow, retirement timeline and other goals simultaneously, before you commit.

What to do if your startup fails financially

Startup failure is common and it is not something to be anxious about, but something to plan for financially. Often the startup experience can be a good value add to your resume and show that you have the drive and resourcefulness to handle high levels of ambiguity. So use your startup experiences and skills to obtain a leadership role at a company that values these skills.

In the process of building your startup, you may have done a lot of networking and met smart people. You can reach out to them and see if they can hire you or recommend you for a role. Many VC firms hire ex-founders and operators so do not forget to keep in touch with your VC friends.

The bottom line

Founders who navigate personal finances well tend to share one habit: they treat their own money with the same rigour they bring to their business. They know their runway, they separate their accounts, they plan for the costs that do not show up until they do.

The startup bet is already a bold one. The goal is to make sure your personal finances are stable enough that when hard decisions come, and they will, you are making them with a clear head. If you want to model your own situation, Planwell lets you input your exact income, expenses, assets and goals and get a personalized plan in minutes, without the generic advice that does not account for how founders actually live.

About the Author

Arundhati Sampath is the founder of Planwell, a financial planning tool designed to help people model life transitions such as startup journeys, career breaks, and early retirement.

The post How to Plan Personal Finances as a Startup Founder appeared first on BYVI.

In 2009, a PhD student, Lester Mackey, entered a competition Netflix had created to find the best movie recommendation algorithm. The prize was a million dollars. The rules were simple: beat Netflix’s own algorithm by 10%. There was just one problem: the dataset was too large to fit on a laptop.

Mackey asked a labmate who would become his co-founder if they could build something to handle it. They wrote 600 lines of code over a weekend. Mackey competed. He tied for first place on the improvement score. He lost the million dollars because the other team submitted their entry 20 minutes earlier.

That 600-line weekend project became Apache Spark. The open source standard for data processing used by most of the world’s largest companies. The seed of Databricks, now valued at over $130 billion.

Reynold Xin, Databricks’ co-founder, tells this story without drama. That’s partly his style, but it’s also because he understands something most founders spend years resisting: the beginning of a great company almost never looks like one. It looks like a competition you lose. A product nobody will pay for. And 3 years of misery in the middle of what looks, from the outside, like a success.

When Traction Is a Lie

By 2013, Databricks was a real company with real momentum. Apache Spark had become the de facto standard for large-scale data processing. Thousands of engineers were showing up to conferences. There were selfies. There was what Reynold diplomatically calls “massive open source traction.”

There was also, by the end of 2015, roughly 1M dollars in annual revenue. 3 years in.

“We were a commercial failure for the first 3 years,” Reynold says.

“From the outside it was hard to understand because a lot of the metrics were vanity metrics about open source adoption. But when you looked at revenue…”

And this is a huge trap. Not failure — the appearance of success while the business itself doesn’t work. The metrics that feel good are not the metrics that matter. Thousands of downloads and a room full of raving fans is not a business.

The breakthrough, when it came, had two parts.

First: building actual go-to-market machinery. Databricks was a group of engineers and founders wandering around trying to sell a product. That’s fine as a learning experience — Reynold believes it — but it doesn’t scale. You need the infrastructure of a real business.

Second, and more fundamentally: creating proprietary IP. This is the part founders of open source projects resist most. But Reynold puts it like this:

“Just because we created a massively successful open source ecosystem doesn’t mean customers should pay us for what they’d have gotten for free. There needed to be some value that was justified.”

The vision didn’t change. The go-to-market and the proprietary layer had to be built around it. One without the other is either a movement or a charity.

What Mike Did That Almost Nobody Does

Before Ada, Mike Murchison was building a consumer app. It was growing. And then, the way scaling companies do, it started treating its own customers as a liability.

“Customer service got worse the bigger we got,” he says. “We went from knowing our users’ names, craving their feedback — to our customer service operations being focused entirely on reducing contact.”

He started cold-calling VPs of Customer Experience to understand why. He talked to twelve of them. All twelve said the same thing: we talk to our customers less as we grow. Customer service is a cost center. I am literally compensated on how much I can reduce contact this year.

“When I heard that maybe the twelfth time in a row,” Mike says, “I started to develop a thesis.”

Here is where Mike did something most founders don’t. He didn’t go build. He went back to those same twelve VPs and asked if he and his co-founder could join their teams — as customer service agents. Seven said yes. He spent a year in the weeds, working support queues, learning the operations from the inside, before he revealed what he was actually building.

“We learned very manually how to provide great service at scale,” he says. “That has informed our approach and continues to today.”

This is the thing that separates deep founders from competent ones. Mike didn’t just understand the problem intellectually. He lived it. He wasn’t studying the problem from the outside. He was the problem.

The Only Advice That Actually Matters

Reynold texted his co-founders before going on stage: What advice would you give?

Several responded. Then their CEO, Ali Ghodsi, replied: “I disagree with all of the above.” And offered a single sentence.

“Nothing can substitute for creating a product that is 10 times better than something else that exists.”

That’s it. Not 10 times better than the closest alternative at some features. 10 times better, full stop. Or going zero-to-one — solving something that was previously impossible. Do either of those things and you have a business. Do neither and no amount of execution, culture, or fundraising will save you.

“The 10x doesn’t mean exactly ten times,” Reynold clarifies. “It means it can’t be something fairly incremental if you actually want to build a very successful business.”

Mike adds the practical path:

“When you start a company, you’re so resource-constrained. You look at successful companies and think – they have resources, infrastructure, distribution that I don’t have. But there is one thing you can genuinely be the best in the world at: understanding your problem more deeply than anyone else.

That’s the edge. Not capital. Not talent. Not even technology. All of which you can eventually acquire. The edge is understanding. And it’s available to everyone who’s willing to actually do the work of getting it.

When Reynold talks about customer obsession, he’s not talking about a cultural value written on a wall. He means something specific. He was doing 10 customer meetings himself — not his sales team, not his product managers — over a two-week stretch that spanned an American holiday. He texts design partners on a first-name basis. He asks them not “what’s wrong with the product” but “what are your concerns” and “what else can we do for you.” The distinction matters. One question narrows, and the other opens.

Right now, those conversations are surfacing a single theme: AI token costs.

The world has swung from “use more, use more, use use use” to “I burned through my entire annual budget in the first quarter, now what?” Every company is having this conversation. That’s a product opportunity. You only find it if you’re in the room.

What the AI Era Actually Changes

Both founders are building AI companies and running their businesses on AI. Here is their take.

The cost of writing software is falling fast. A two-person team can do work that used to require a hundred people. The arbitrage opportunity for small companies right now is speed — moving while large incumbents are still figuring out their governance and approval processes.

Mike says:

“Showing up as a two-person team that can do the work of a hundred-person team really does make a difference, more so than it used to.”

Taste Is the New Moat

There’s a question that used to be fairly simple for founders: what makes us defensible? You’d answer it with technology, patents, network effects, switching costs. The standard toolkit.

Mike Murchison thinks the answer has changed:

“The most important thing right now is taste. It’s never been more important for you to say: this is what an amazing experience looks like, and to be able to paint it.”

He’s not talking about design sensibility. He’s talking about something harder: the ability to hold a precise, uncompromising vision of greatness at the exact moment when building has never been cheaper, faster, or more democratic.

At Ada, almost everyone in the company is now creating pull requests, contributing directly to the product, regardless of their background. Non-technical people shipping features. The cost of participation has collapsed.

That premium shows up in a specific way: the people who will win are the ones who can clearly say “this is not good enough”, before the market tells them. Not after the NPS score comes back. Not after a competitor ships. Before. The vision has to be internal and precise, not external and reactive.

Reynold agrees, and then adds a wrinkle that makes the argument more complicated.

“If you assume the cost of software continues going down,” he says, “one natural outcome is that it becomes easy to copy. You can reduce taste by poor judgment, yes. But you can also simply copy it.”

His point:

Taste is real and it matters, but it isn’t a durable moat by itself. What can’t be copied overnight is the oldest, most unsexy list in business — brand, distribution, trust.

“A red Ferrari is a red Ferrari,” Reynold says. “You can’t have a red Xiaomi and sell it for half a million dollars.”

For founders at the earliest stages, this sounds like bad news — you don’t have any of those things.

Read it differently and it’s actually clarifying. In the short run, taste and speed are your moats. Move fast, build something extraordinary, get close to your customers before larger players figure out their governance process. In the long run, you’re racing to convert that early advantage into brand, distribution, and trust before the window closes.

The companies that do both — start with taste, compound into moats — are the ones that become generational.

The question to ask yourself isn’t whether you have taste. It’s whether you can define, specifically, what “extraordinary” looks like in your market — and whether every decision you’re making right now moves toward it or away from it.

The Things Successful Founders Don’t Usually Say

Reynold says something at the end of this conversation that most successful founders won’t say. Asked whether founders should start companies, he doesn’t give the standard answer.

“If you’re a strong engineer who can find a well-paying job,” he says carefully, “the expected value of working might actually be higher. The risk of failure is pretty high.”

He says it not to discourage anyone, but because he’s being honest in a room full of people who are about to make a consequential decision. He follows it with:

“That said, fail once, fail three times, keep going. There’s nothing wrong with it. The rate of learning is the highest with entrepreneurship. You will develop a skill set that your peers who went to big companies simply won’t have.”

Mike extends it: “The downside is so much more minimal than you think. And there’s so much more upside in starting something than most people realize.”

Both of them, in different ways, are pointing at the same thing. The failure people fear is rarely as catastrophic as it feels in advance. The learning is irreplaceable. And the environment you choose — the five people you spend most of your time with, the peer group that shapes what you think is ambitious and what isn’t — turns out to be one of the most important decisions you make.

“You can be the best in the world at understanding your problem,” Mike says. “You get to choose who influences you to be more ambitious. Those are both in your control.”

That’s the brick. The rest is building.

About the author

Lidia Vijga

Lidia Vijga is a content engineer focused on building in-house growth engines and training AI agents that help companies scale content and dominate brand visibility across traditional and AI search. She is also co-founder of DeckLinks, bringing firsthand founder experience in building and scaling technology products.

The post How to Build a $100B Company, Brick by Brick appeared first on BYVI.

A VP of Customer Success at a Series C SaaS company spent months building what she was proud of: a proper CS machine. Health scores tied to product usage. Automated alerts at 90, 60, and 30 days before renewal. A QBR cadence. A team of CSMs who knew their books of business cold.

Then one of her best accounts churned. She found out why a year later: the customer had quietly shifted strategy 9 months earlier, following a new CFO hire. There was no signal in the data. No flag in the CRM. No alert. Just a renewal conversation that started three weeks before expiration, and a customer who had already made up their mind.

This is the central contradiction of proactive customer success: most of it isn’t proactive at all.

The CS Mantra That Became a Myth

“Proactive customer success” has become the operating philosophy for virtually every CS org at growth-stage companies. Conference tracks are dedicated to it. Job descriptions demand it. Entire platforms have been built around it.

And yet churn keeps happening. Net Revenue Retention keeps disappointing. And CS leaders at Series C companies (companies that have already figured out product-market fit, that have mature CS organizations, that are spending real money on tooling) are the ones who feel this most acutely.

These teams aren’t failing for lack of effort. The tools they’re running on were designed around a narrower model of what knowing your customer actually means.

What Actually Breaks at Series C

At Series A and B, customer success is still a relationship business. You have a manageable number of accounts, relatively similar customers, and enough CSM bandwidth to maintain deep context on each one. You can look across 3 to 5 systems to understand an account and you can get away with outreach that treats your base as a single segment. It works because the math still works.

Series C breaks this entirely.

“Headcount cannot solve the issue,” says Archana Somasegar, founder of MineMe, a customer intelligence platform.

“As your customer base diversifies, you need to bring data into one place to raise real opportunities and separate signal from noise.”

Archana Somasegar, founder of MineMe

The portfolio grows. Customer profiles diverge. The CSM-to-account ratio stretches. And the “proactive” motions that worked at smaller scale (the health score dashboards, the renewal-triggered workflows, the QBR cadences) start to collapse. Not because the tools are bad, but because they were designed for a world that no longer exists.

The strategic imperative also shifts. At Series A/B, the name of the game is new logo acquisition. By Series C, the growth model has to run on retention and expansion: maximizing revenue from an existing, diversifying customer base. That requires a completely different CS playbook, and most organizations are still running the old one.

The Blind Spot in Every CRM

Here is the architectural problem: your CRM was built to track what is happening in your world. Product usage. Support tickets. NPS responses. Login frequency. Health scores. It is, in every meaningful sense, a record of your customer’s relationship with your product.

A new CFO who comes in with a mandate to cut SaaS spend. A layoff that eliminates the internal champion who drove your product’s adoption. An earnings call where the CEO announces a strategic pivot away from the use case your product serves. A competitor product launch that reshapes their roadmap.

These are the signals that actually predict churn, and almost no CS platform ingests them natively.

This is the proactive customer success trap: teams invest in faster, more sophisticated reaction (better health scores, more granular triggers, more automated playbooks) and call it proactivity. But reacting quickly to signals your own system generates is still, structurally, reactive. You’re just shortening the lag.

The Real Difference Between Reactive and Proactive Customer Success

The distinction is more consequential than most CS leaders acknowledge.

Reactive CS, even when fast, operates inside a closed loop: something changes in your data, a workflow fires, a CSM acts. The ceiling on this approach is determined by the quality of data you already hold, and that data is almost entirely backward-looking.

Truly proactive CS operates in open context: the customer’s business environment is continuously modeled, signals from outside the product are weighted alongside signals from within it, and outreach is triggered by what’s happening in the customer’s world, not just in yours.

This requires a different kind of tooling and a different organizational mindset. Most CS platforms, even sophisticated ones, haven’t been built this way. Their underlying data models are still centered on the vendor relationship, with AI layered on top to summarize or generate templates.

The difference matters at scale. Static models become stale. Customers change faster than CS teams can update their playbooks. And by the time the lag shows up in your health score, you are already in a reactive posture. You just don’t know it yet.

3 Questions Every Series C Customer Success Leader Should Ask

The path forward doesn’t start with buying new tooling. It starts with an honest audit of what your current motion is actually optimized for.

1 . Does your CS data model include anything that happens outside your product?

Not just what the customer does in your platform, but what’s happening in their business. Hiring signals. Earnings reports. Executive changes. Product launches. If the answer is no, your “proactive” motion is operating on a fraction of the relevant data.

2. Do your CSMs have any systematic way to know when a customer’s business context has shifted, before the customer tells them?

The best CS teams at Series C aren’t waiting for customers to surface problems. They’re surfacing context the customer didn’t know they needed to share.

3. Are your “proactive” outreach triggers based on what just happened, or on a model of where the customer is heading?

There’s a meaningful difference between a health score that reflects last quarter’s usage and a model that predicts next quarter’s risk. Most organizations are running the former and calling it the latter.

The Customer Success Practices That Look Proactive But Aren’t

The habits that need to change aren’t only about tooling. They show up in daily practice: QBRs built around metrics that matter to you rather than to your customer, outreach cadences that only accelerate when a renewal is 30 days out, CS teams positioned primarily as a buffer between customers and support queues. These aren’t fringe behaviors. They’re industry defaults, and they share the same underlying logic: the system is oriented around what’s easy to track inside your own ecosystem, not what actually predicts whether a customer renews.

That’s not a criticism of the teams running these motion. It’s the natural output of a category built around vendor workflows rather than customer realities. The tooling caught up to the relationship layer of CS years ago.

The intelligence layer (the part that models what’s happening in a customer’s world, not just in your product) is exactly what MineMe was built to solve.

The CS leaders who close that gap first won’t just see lower churn. They’ll find themselves having fundamentally different conversations: not “here’s how you’ve been using the product this quarter,” but “here’s what we noticed in your business, and here’s how we can help.” That shift changes the entire nature of the relationship.

A team that monitors your product usage is a vendor. A team that watches your world is something much closer to a strategic partner.

And for Archana Somasegar, the real goal was never purely defensive to begin with. “NRR is an exceptional metric,” she says. “You want to reach your churn goals, but importantly, growth opportunity is critical.” A CS org built to earn that growth isn’t built on better dashboards. It’s built on paying attention to the right world.

Insights by Archana Somasegar

Archana Somasegar is the founder of MineMe, an AI-native customer intelligence platform that combines real-time external signals (funding rounds, executive hires, product launches) with CRM data to predict next-best actions and surface expansion and retention opportunities for customer-facing teams.

About the author

Lidia Vijga

Lidia Vijga is a content engineer focused on building in-house growth engines and training AI agents that help companies scale content and dominate brand visibility across traditional and AI search. She is also co-founder of DeckLinks, bringing firsthand founder experience in building and scaling technology products.

The post Why Proactive Customer Success Keeps Failing at Series C appeared first on BYVI.

Every CMO I know has the same private suspicion right now: the things that used to be hard are getting cheap, the things that used to differentiate are getting commoditized, and the only thing that still feels defensible is the way customers feel about you. A recent Startup Grind panel — featuring marketing leaders from Figma, Vanta, Brex and beyond — landed on the same conclusion from five different angles.

Brand is what people feel you stand for — not what you ship

The most quoted line of the night may also be the simplest: “At the end of the day, your users, your community and the people who love your product want to know what you stand for.”

Copy can be copied. Features will be replicated by a model in 3 weeks. The emotional and values-based residue your company leaves behind — what one panelist called the “beyond product functionality” layer — is the only asset that doesn’t get cloned.

For marketing leaders, the implication is uncomfortable: a lot of what counts as “marketing output” right now contributes nothing to that residue. It generates impressions and timeline real estate while leaving the underlying brand exactly where it was.

The launch treadmill is a vanity metric

The panel was especially blunt about shipping cadence. “It’s a trap to fall into the launch treadmill because it makes you feel very active if you’re producing all these artifacts,” said one speaker. “People like your posts on LinkedIn and Twitter. But the reality is it’s really meaningfully shifting whether customers are adopting, using, engaging deeply with your product.”

Frequent launches feel like progress because they look like progress. Internally, they fill review meetings; externally, they get reposts. But the metric that matters — does this change how deeply customers use the thing — is rarely the metric being optimized. Worse, teams slip into “a binary, we launched it, we’re done kind of mentality and then onto the next,” which severs the marketing function from the actual adoption curve.

The fix is counterintuitive in an industry built on momentum: launch less, bundle more, and let the timing serve the story. “What you do could mean pushing back a launch, collapsing launches together so you have a bigger moment, telling the more thematic bundle.” A thematic moment that lands is worth 10 “launches” that scroll past.

A caveat about pace: don’t read OpenAI’s cadence as a benchmark. As one panelist explained, “the big labs, you’re building from model capabilities forward” — they ship because their job is to educate the world about what models can now do. Product companies “shift from customer needs backwards,” which is a fundamentally different game. Importing the frontier-lab tempo into a vertical SaaS company is cargo-cult marketing.

Pick your enemy, not your competitor

Brand is sharpened by what it’s against. But the panel was careful to distinguish picking an enemy from picking a fight.

For early-stage companies, the most productive enemy is usually the status quo itself. “For us, our first enemy was a taxi, not Lyft,” said the Uber alum on the panel. That framing did something a competitor jab could not: it told users what kind of world the company believed in. Taxis weren’t another app; they were a daily indignity. Naming the indignity gave Uber a meaning beyond the product.

For more established companies, going after a much larger incumbent remains the safest, highest-leverage play. Scott from Brex described the famous Concur campaign: “We turned their name into a curse word, Concur…rrrgh!. And when you have a competitor that’s significantly larger, it’s a very safe bet and a great way to get more mind share if you go with them.”

Anthropic’s Super Bowl spot aimed at OpenAI was, in his read, the same playbook scaled up — “incredibly unexpected and because of that was one of the most bold things I’ve seen.”

The harder question is what to do with rising peers. The traditional answer is to ignore them and refuse to lend them legitimacy. That instinct is being rewritten. “The prevailing wisdom is to do nothing there and not recognize them,” Scott said, “and my prediction is you’re going to see more of [direct rivalry] because markets are getting some more competitive.” Harvey vs. Legora is the current poster child: “The fight becomes the story and it elevates both of the projects.”

A warning, though: this only works from a position of clarity. “You have to come at it from a position of confidence and clarity about your why and what you stand for,” said Figma’s speaker, who pointed to the company’s own decade-long stance — advocacy for design as a discipline — as the anchor that lets sharper messaging work. Without that anchor, “done wrong, it can have really negative repercussions on your brand.”

Build the marketing team your funnel actually needs

The most useful operational thread of the night was on hiring. The panel pushed back on the default reflex of “we need a head of marketing” with a more diagnostic frame: hire to the bottleneck.

In this era, the most contested hire is product marketing.

“Product marketing is probably one of the most sought-after hires right now,” and for good reason: the central job is to “figure out your why — what makes you different, what makes you differentiated in the market. That, I think, is the north star of this current time and era.”

When every competitor can ship the same feature in a quarter, the team that can articulate why their version matters wins.

Two practical updates to the old org chart are worth flagging.

First, the disciplines are blending. “With the aid of AI, it’s much easier to flex into demand generation or into brand storytelling,” and a strong product marketer can now read a demand-gen dashboard without a translator. The cycle from idea to execution has collapsed — “you can go from idea to execution in a much shorter cycle time than before” — because the ad ops, design, and analytics layers that used to require entire teams can now be carried by one operator. Expect more from each hire, but expect to need fewer of them.

Second, the founder is part of the marketing team whether they like it or not. The panel was unanimous that the best founders “intuitively care about brand and communications and how you tell a story.” When that’s true, the first hire can be a growth marketer rather than a narrative one — the founder is already carrying the narrative. When it’s not, product marketing has to come first, because nothing the rest of the team builds will compound without a sharp answer to “what are we, and what are we against.”

The pricing pendulum hasn’t landed yet

A short detour on pricing, because every CMO is being asked about it. The fashionable claim that “per-seat is over” is, in the panel’s read, overblown. Consumption pricing is real and rising, but it carries its own friction: high cognitive load for users trying to value individual tasks, and runaway-spend anxiety from buyers who answer to a CFO. The pendulum will swing back. The likely steady state is a hybrid — consumption bucketed into predictable tiers, with thresholds that trigger upgrades rather than running counters that trigger panic.

A test you can run yourself

The most operational takeaway from the panel was also the most testable. Quiz every person in your company on two questions:

1. Who are we building for
2. How are we different

If everyone answers the same way, you’ve won 75% of the battle.

Most companies won’t pass. The fix is not a brand refresh. It’s writing down — in the founder’s voice, not the agency’s — a half-page of prose that backs up the positioning, and then making sure the website headline reflects it without jargon.

“Memorialize your vision beyond your head,” the panel said, because if it only lives in the founder’s head, the company has no brand asset at all.

Today, that half-page may be the most valuable thing the marketing team owns. Product gets copied. Pricing gets matched. But the story — the one your team tells with conviction, in your founder’s own voice — is yours alone. And that’s the moat.

About the Author

Lidia Vijga

Lidia Vijga is a content engineer focused on building in-house growth engines and training AI agents that help companies scale content and dominate brand visibility across traditional and AI search. She is also co-founder of DeckLinks, bringing firsthand founder experience in building and scaling technology products.

The post Brand Marketing: The Only Moat Left Is How You Make People Feel appeared first on BYVI.

Sam Altman’s Stripe Press talk delivered something more useful than headlines: a handful of observations, for founders, about how to actually build a company in this moment. These are the notes from the room, and what’s stuck with me a few days later.

The “idea guys” are winning again

For years, “idea guy” was an insult. If you couldn’t ship, you didn’t matter. Altman flipped that. His read: non-technical founders who deeply understand a user and a problem now have an unusual advantage, because so much of the building part is becoming AI-assisted. Technical talent isn’t suddenly unimportant, but it’s no longer the only ticket in.

That clearly landed for me because I’ve lived the old version. In 2018, when I was building my first startup, we had to hire a CTO and two software engineers before we could put anything meaningful in front of a user. That was the table stakes. The product idea wasn’t unusually technical, and the user insight was solid, but the only way to translate it into something real was to assemble a team and a payroll first.

Now I keep meeting solo and two-person teams who, a few years ago, would have needed exactly that same setup – and they’re already shipping. They’re not doing it well because they’re great engineers, they’re doing it well because they have taste, judgment, and proximity to the user, and they’re letting AI fill in around that. The early validation that used to cost 3 hires now costs a weekend.

AI adoption is a CEO job, and a data-access decision

The clearest pattern Altman has seen in companies that successfully adopt AI: the CEO drives it personally. He cited Tobi Lütke’s mandate at Shopify as the archetype – leaders getting their hands dirty building with the tools, not just signing off on someone else’s pilot.

I’ve had very good results running autoresearch with local qwen 3.6 26b model as long as I had a simple vibed pi “advisor” extension that allowed it to periodically ask GPT 5.5 for ideas. I think this direction has a lot of merit.

— tobi lutke (@tobi) May 18, 2026

The other half of that pattern was less comfortable. The teams getting the most out of AI are giving their systems what he called “uncomfortably permissive” data access — codebase, Slack, email, recorded meetings, the works.

Two- and three-person startups are running everything through a single Slack channel: planning, coding, deploying, log analysis, customer support. It doesn’t scale to a Fortune 500 yet. But for small teams, the upside-to-risk ratio is currently very favorable.

I’m sitting with this one. There’s a real tension between “give the AI everything and move faster” and “this is sensitive data we said we’d handle carefully.” I don’t think it’s resolved. But pretending the tradeoff doesn’t exist is worse than picking a deliberate point on the spectrum.

OpenAI wants to be the utility, not the empire

This was the line that surprised me most. Altman was explicit: OpenAI does not want to gobble up the entire value chain. The aspirational analogy was Stripe, a piece of infrastructure that’s tightly aligned with its customers, runs on low margin and high volume, and wins when its customers win.

He doesn’t think huge AI margins will hold long-term anyway, because switching costs are staying low. Models keep getting substitutable. When one gets clearly better, customers move. That dynamic pushes toward utility economics, not lock-in economics.

Shared convictions beat shared chemistry

The part of the talk I didn’t expect to find super practical was about managing brilliant, difficult people. Altman attributed a chunk of OpenAI’s early success to getting a room of people, each of whom believed they were the smartest person in it, to actually collaborate.

His read on why it worked: they didn’t need to like each other. They needed to share 3 convictions:

1. that scale matters
2. that concentrating resources matters
3. that running one focused research program (rather than many small bets) mattered

Those shared beliefs let them tolerate personal friction that would have wrecked a team with less alignment.

I keep thinking about this in the context of small teams. Founders often optimize for “do we get along?” Altman’s framing suggests a sharper question: “do we agree on the few things that actually determine whether we win?” Those are different filters, and they produce different teams.

The founder-relationship signal

Quieter point, but worth flagging: he said one of the strongest predictors of a startup working is that the founders have known each other for a long time. Teams formed right before launch — co-founder dating, hackathon pairings, mutual-friend introductions made the week before incorporation — are statistically less reliable.

This isn’t a new observation, but it lands harder coming from someone who has watched thousands of teams. If you’re forming a company with someone you met last month, the bar to make it work isn’t impossible — but it’s higher than the founder-talk economy would have you believe.

The agent moment that everyone will remember

The lightest thread, and the one I’ll close on: Altman talked about how strange it still feels when agents do something unexpectedly human. He gave the example of an agent buying itself a novelty gift, and of asking a model how it wanted its own launch handled — and getting a surprisingly specific, slightly funny answer.

He wasn’t making a philosophical point. He was acknowledging that even people who built these systems still get briefly thrown when the systems act like they have preferences. I appreciated the honesty of it. Most of what’s said about AI in founder circles right now is either utopian or apocalyptic; almost none of it captures the actual texture of using these tools day-to-day, which is that they’re extraordinary and occasionally just weird.

Sam Altman’s closing advice

His closing advice for founders was anti-dramatic, but it has teeth: durable companies are the ones positioned so model improvements help them rather than erase their value. As he put it, you want to “be on the side of hoping that AI gets smarter.”

The flip side, in his telling: if your product’s value rests on papering over a current model weakness — a workflow that exists because the model can’t yet do something — the next model release will eat it. The same improvement that makes everyone else more productive quietly deletes your moat.

It’s good advice precisely because it’s actionable. Nobody who has to ship next week needs a worldview about AI. They need a heuristic, and that one holds up: build so smarter models make you more valuable, not less.

About the Author

Lidia Vijga

Lidia Vijga is a content engineer focused on building in-house growth engines and training AI agents that help companies scale content and dominate brand visibility across traditional and AI search. She is also co-founder of DeckLinks, bringing firsthand founder experience in building and scaling technology products.

The post Sam Altman’s Advice for Founders from Stripe Press 2026 appeared first on BYVI.