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

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

At Stripe Press, Klarna and Stripe took the stage to make a case that might surprise most e-commerce teams: the biggest threat to your sales in an AI-driven world isn’t a bad checkout experience – it’s never being found in the first place.

The talk, centered on agentic commerce, offered a ground-level look at an infrastructure shift that’s already in motion. AI traffic to e-commerce sites is up 393% year-on-year according to Adobe’s Q1 2026 report. But the opportunity that number represents is being quietly squandered, because the product catalogs agents are being sent to read were never built for machines.

The Invisible Catalog Problem

This was the statistic that opened the discussion:

That’s not a rounding error. It means the majority of product pages that an AI shopping agent visits today are returning incomplete data. And incomplete data means the agent can’t confidently recommend the product. From the agent’s perspective, those items might as well not exist.

To make the point concrete, the speakers walked through a case study of a major activewear brand. The brand had 160,000 products in its catalog. Of those, only 10,000 (just 6%) were actually discoverable by AI agents.

“For that reason, the AI agents are not able to confidently compare, rank and recommend this product.”

Without GTINs, agents couldn’t build complete product cards. Without product cards, they couldn’t make recommendations. The brand’s catalog was effectively invisible at the most important new layer of commerce.

The framing was direct and simple: “If you can’t be found in this layer, your product doesn’t ever have a chance of being purchased at all.”

3 Attributes That Unlock AI Discoverability

The talk distilled the discoverability problem down to 3 catalog attributes that every merchant needs to get right.

1. GTIN (Global Trade Item Number)

GTINs are the connective tissue of agentic commerce. They’re the identifier that lets an AI agent match your product listing against the same item sold by other retailers – enabling comparison, ranking, and confident recommendation.

2. Shipping Cost

Agents don’t rank on product price alone. They rank on total price. If your shipping cost isn’t part of your feed, the agent is presenting an incomplete picture to the consumer, and your product will lose out to one where the full economics are visible.

“If you’re not giving a full picture to the agent and the consumer, your product won’t be picked.”

3. Delivery Time

When a consumer tells an agent “I need these running shoes by Friday,” the agent filters its results to products where delivery by that date is confirmed. It doesn’t guess or check elsewhere. If your delivery window isn’t explicitly stated in your product feed, your item is excluded from that search entirely — no matter how good the price or how relevant the product.

All 3 attributes are prerequisites, not nice-to-haves. The agents doing the searching require structured, machine-readable data to do their job – and merchants who haven’t built for that are already falling behind.

Klarna’s Discovery Layer

Behind the scenes, Klarna has built the infrastructure designed to power agentic product discovery at scale: 100 million unique products, 400 million merchant offers, 4,300 subcategories, and one million merchant partners — all surfaced through a single API that’s normalized across merchants, LLM-optimized, and protocol-agnostic.

The pitch is straightforward: agents query the graph, get structured product data in real time, and can reliably compare and rank items across different sellers. As the speaker described it: “This graph really is what allows your products to be found.”

Rethinking How Payments Work in Agentic Flows

If the first half of the talk was about discovery, the second was about what happens after an agent finds the right product, and why the payment handoff is more complicated than it looks.

In traditional e-commerce, the buyer and the payment processor are in the same session. In agentic commerce, they’re not. The agent acts on behalf of the buyer, but the merchant still needs to process the charge. Alison from Stripe framed this as a fundamental separation:

“Payment intent is being separated from payment processing.”

To solve this, Stripe created Shared Payment Tokens (SPTs). The concept works like a sealed package: the agent creates a token scoped to a specific seller, for a specific timeframe, containing the buyer’s payment credentials. That package is handed to the merchant, who opens it, reviews it, and decides whether to process it or decline.

“You’re able to scope it to a specific seller, a specific time, and then you issue that to the seller and the seller at that time when they receive it, can open it up and they can say, I like what I see, I’m going to process as payment.”

Crucially, SPTs work with existing infrastructure — existing network tokens, device tokens, Klarna tokens. When Stripe launched SPTs, they supported cards only; Klarna support has since been added, with expansion to 100+ payment methods underway through Stripe’s growing ecosystem.

What This Means for Merchants Already on Stripe

For merchants already using Stripe and Klarna, the practical lift is close to zero. Agentic flows reuse existing Stripe components. Existing fraud configurations, Radar risk signals, and payment method settings carry over automatically. As Alison put it: “If you have enabled Klarna, it is already enabled in your agentic flows.”

Product catalogs can be fed into the discovery layer via CSV upload or import APIs, with the option to plug in existing commerce stack integrations rather than rebuilding from scratch.

In Agentic Commerce, a Failed Transaction Has No Recovery Play

One of the more striking moments in the talk was the discussion of what happens when an agentic transaction fails, and why it’s categorically different from a regular abandoned cart.

In standard e-commerce, a failed checkout is painful but recoverable. You can retarget, send a follow-up email, offer a discount. The consumer is still reachable. In an agentic flow, that path largely disappears. As the speaker explained:

“That’s very different when there’s a failed experience in an agentic flow, because that’s something that a consumer might not recover from, at least not quickly.”

Worse, a failed transaction doesn’t just cost the merchant a sale, it damages the consumer’s trust in the agent itself. “They might not trust that agent very quickly again.” This changes what agents optimize for. Rather than pure conversion, they weight for reliability in roughly this order: consumer trust, payment flexibility, post-purchase liability, and then approval certainty.

Account Linking Challenge in Agentic Commerce

The talk closed on one of the more existential concerns merchants raised: how do you maintain a direct relationship with your customer when an agent is in the middle?

Personalization, loyalty programs, purchase history, recommendations — all of it depends on knowing who the customer is. When an agent intermediates the transaction, that connection can break. As Alison noted:

“That’s probably one of the number one pieces of pushback we hear from merchants when we’re talking about agentic commerce – how do I maintain that relationship with my customer?”

Account linking is the proposed answer, connecting the consumer’s identity across agentic flows so merchants can preserve personalization and relationship data. “Solving the account linking challenge is what will allow them to really bring that rich experience and the personalization into agentic flows.”

The Takeaway

Agentic commerce isn’t on the horizon, it’s already scaling. The 393% growth in AI traffic is being driven by agents that are actively shopping, comparing, and recommending right now. The merchants who will win in this environment are the ones who’ve done the foundational work: clean GTINs, complete shipping data, accurate delivery windows, and product feeds built for machines to read.

The checkout experience still matters. But by the time a consumer reaches it, the hardest work is already done — or it isn’t. In agentic commerce, the sale is won or lost in the catalog, long before anyone clicks buy.

About the Author

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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.

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

If you’re gearing up to raise a seed round in 2026, the playbook has changed. At a recent Startup Grind panel, a group of seed investors spelled out exactly what they’re looking for — and what’s getting founders passed over. Here’s what you need to know.

The bar has moved: come with traction, not just an idea

The cost to build has collapsed. AI tools mean a motivated founder can ship a working product in weeks, not months. Investors know this, and they’ve adjusted their expectations accordingly.

“The cost to build is almost zero these days”

That means showing up to a seed meeting with nothing but a deck and a vision is increasingly not enough. Investors now expect a working prototype at minimum — and if you have early customers, even better.

One investor pointed to a portfolio company called Nectar Social, an AI platform for community management, as an example of the new standard: they raised their seed round with a working product and actual paying customers already on board.

The market is both flush with cash and brutally concentrated

There’s a strange tension in seed markets right now. Capital is abundant — “tons of money in the system,” as one investor put it — but it’s flowing to a smaller and smaller number of companies. A handful of AI startups are vacuuming up enormous rounds, while the rest of the market fights for scraps.

One investor described this openly: “It’s a hard environment to be in where this much money is being concentrated in so few companies.” They also noted that early venture — the art of backing founders before outcomes are obvious — is getting squeezed out as the market turns more transactional.

The best investors still want to fund before things are known. That window exists. But it’s just narrowing.

What investors are actually looking for in founders

Credentials, pedigree, and even age matter far less than people assume. What investors are actually reading for is a specific set of signals.

A life mission, not just a startup idea

The bar isn’t “I have a good idea.” It’s “I am the person who was always going to build this.” Investors are betting on a 10–12 year relationship, and they need to believe the founder will survive the emotional ride that entails.

“If you have a vision and you feel so clear in your heart, in your gut that this is what is your life mission,” one investor said, “and you can articulate it and actually find the investor that sees it with you and can be your partner on this journey — I think it’s fascinating.” What they’re looking for is a missionary, not a mercenary.

Is there a real “why” behind what you’re building? That’s the question they’re asking themselves as you pitch.

Boldness and a contrarian point of view

Safe ideas don’t produce outlier returns, and investors know it. They’re drawn to founders who hold views that feel a little uncomfortable — people willing to bet on a future most haven’t seen yet. Being bold doesn’t mean being reckless; it means being willing to articulate a conviction clearly even when the room isn’t sure yet.

The ability to build and sell

One investor put it plainly: “The first thing I look for is: can you build and can you sell?” These are often treated as separate skill sets that get split across a founding team, but at the earliest stage, investors want to see that the lead founder can do both — or at least credibly lead both.

The selling piece matters especially for storytelling. “That narrative is so critical,” one panelist said, “because it defines how you’re going to hire people you shouldn’t have any business hiring, how you’re going to get funding you shouldn’t have any business getting, and how you win your first customers.” A founder who can build but can’t tell the story is leaving enormous leverage on the table.

Hands-on involvement in the product

The panel was clear: investors want founders who do the work, not just manage the people who do. They’re watching for the founder who is in the product details day in and day out, setting the standard by example rather than delegating from a distance.

One investor described an Uber leader as the model: deeply embedded in product decisions, going to Singapore to interview an intern to understand local market needs firsthand, still working alongside the team at midnight. That kind of operational presence signals both genuine commitment and the self-awareness to know where value actually gets created.

Relentlessness and speed, at any age

“That relentlessness, that speed” — one panelist repeated this phrase as if it’s the filter everything else runs through. The good news: this is a mindset trait, not a demographic one.

The panel was explicit that great founders come from all age groups. Younger founders tend to be more technical and carry fewer preconceived notions — which is part of why the panel sees a “renaissance of weird products” as building gets easier. But older founders are also coming off the sidelines in this moment, and investors welcome that. What they’re evaluating is drive and mental agility.

Consistent follow-through over time

A great first pitch is table stakes. What actually builds investor conviction is a pattern of doing what you said you’d do.

One investor described being a fan of founder updates for exactly this reason: “Meeting the same person multiple times and saying ‘I’m going to do a thing,’ and then you show up the next time and you did that thing — plus more.” If you’re building investor relationships before your raise, treat every interaction as a chance to demonstrate follow-through. It compounds.

A culture of relentlessness built around the mission

Finally, investors are looking at the team you’re assembling, not just you. Are the people around you bought into the mission? Do they want to win, or are they just taking a paycheck?

“You need to build a culture of relentlessness around hiring the right people that are really bought into the mission and really want to win,” one investor said. This ties directly to how you lead — whether intensity in your company comes from inspiration or from pressure. Investors can usually tell the difference.

Narrative is a competitive weapon

In a crowded market, your story is your differentiation. One investor described seeing five companies pitching in the same space, and then a sixth walked in with a completely different quality of insight: “They told the story in such an authentic way that they have an insight of what’s coming in the short term and what they see in the long term.”

Investors want to fund the company that will be big in three to five years, not the best version of what exists today. “We’re not looking for the incremental sixth player, even if the market’s very big,” one investor said. They’re looking for founders with a genuine, forward-looking viewpoint — not just pattern-matching on what’s already working.

On valuations: don’t optimize for the highest number

Seed valuations in AI are at all-time highs right now. The panel was clear: that’s a trap.

“You cannot optimize for the biggest valuation,” one investor said. The logic is simple — whatever you raise at, you have to grow into. Take an inflated seed valuation, and you’ve set a high bar for your Series A at a time when “if there is a correction, it can be very brutal.”

More importantly: the right investor is worth far more than a few extra million on the cap sheet. Choose partners who will actually help you, who believe in what you’re building, and who will be useful in hard moments.

On hustle culture: intensity from mission, not mandate

The panel pushed back firmly on the idea that founders need to work themselves into the ground. “996 is the output of culture, not the input point,” one investor said.

The key distinction: intensity that comes from believing in the mission is valuable and sustainable. Intensity that comes from mandated hours is neither. As one investor put it, “If AI is supposed to make you so much more productive, you don’t necessarily need to work like you’re burning out.”

Build a team that genuinely wants to win — not one that’s grinding out of fear. That’s the culture investors want to see.

The bottom line

The 2026 seed market rewards founders who show up with evidence: a product that works, a life mission, a narrative about the future that’s original, and a track record of doing what they said they would. The money is there. The bar is just higher, and the competition for the best investors is fierce.

The investors on this panel were clear: they’re still excited to back people before outcomes are certain. But they need to see enough signal to believe you’re the one.

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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.

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Toronto Tech Week 2026 kicks off May 25–29, and the calendar is wild: 500+ events spread across 30-something neighbourhoods, all happening at once. Great problem to have, tricky logistics. If you’re an early-stage founder, every hour is double-booked, half the rooms are noise, and the ones that actually matter aren’t always the loudest.

These are my top picks if I were running a pre-seed or seed-stage company this year. And this is the filter I was using: investor access, peer-founder density, useful fundraising intel, co-founder and hiring leverage, and the conversations actually worth showing up for.

TiEQuest Summit 2026: Building in Canada for the World

A morning of talks and panels on what it takes to build and scale Canadian companies globally, hosted by TechTO, the community where a surprising amount of Canadian tech quietly happens.

Why you should be there: If you’re an early-stage founder, this is the fastest way to get a read on what’s resonating with Canadian VCs and operators right now. The conversations you overhear in the room are worth more than the talks themselves, and a strong showing here sets up the entire rest of the week. Best for pre-pitch positioning and ecosystem mapping. Register →

Builder Sprint Toronto

Hosted by Highline Beta, a pre-seed VC and corporate venture studio, this is the closing demo + awards night of a week-long sprint where teams build working prototypes against real industry challenges.

Why you should be there: Even if you didn’t participate in the sprint, this is one of the highest-leverage rooms of the entire week for a pre-seed founder. The judging panel and audience are stacked with people who actually write first checks, and Highline’s whole reason for existing is finding 0-to-1 founders worth backing. Show up, ask smart questions, and you’ll be on their radar. Register →

Google for Startups Accelerator: Canada Demo Day

The polished demo day for Google’s Canadian accelerator cohort, investor-heavy audience, well-produced format.

Why you should be there: Two reasons. First, this is a free, no-pressure way to calibrate your own pitch against the cohort that just got picked over thousands of applications. Watch what they show, what they skip, and how they handle the hard questions. Second, the back of the room will be full of investors and program operators — exactly the people you want to be on the radar of when you start your raise. Register →

Reverse Pitch Night with Smart & Biggar

The format is inverted: instead of founders pitching VCs, the investors take the stage and pitch you. They share what they actually want, how they evaluate founders, and what gets a deal across the line.

Why you should be there: Most fundraising advice you read online is generic. This is investors saying out loud, to a specific room of Toronto founders, what they’re funding this year. If you’re 3–12 months away from a raise, this is the single most tactically useful evening of the week. Register →

MaRS Mornings — Hype vs. Reality: Valuation in the Age of AI

A panel of working investors — Brandon Zhao (Two Small Fish Ventures), Parinaz Sobhani (Sagard), Robbie Marks (MaRS IAF) — talking about how AI is reshaping risk and valuations.

Why you should be there: Valuations have moved dramatically in the last 18 months, and if you’re raising as an AI-adjacent company (which is most of you), you need to know what investors are actually willing to pay and why. The room is small enough to ask follow-up questions afterward, which is where the real intel lives. Register →

Toronto Tech Week 2026: Homecoming

The official main stage of the week, with a heavyweight speaker list: Tobi Lütke (Shopify), Nick Frosst (Cohere), Andrew Macdonald (Uber), Alex Danco (Andreessen Horowitz), Lulu Cheng Meservey (Rostra), Emily Hosie (Rebel), Rob Khazzam (Float), and more.

Why you should be there: This is the only day all year where the entire Canadian tech ecosystem is physically in one building. The talks are genuinely good, but the lobby is where you’ll meet the people who matter — fellow founders, investors, operators, future hires. Apply for in-person access if you can, the livestream is a fine plan B. Register →

OneEleven Pitch Showcase

Four standout Toronto startups, five minutes each, pitching to a packed room of investors and operators, followed by a VC-to-VC conversation about current market conditions.

Why you should be there: Watching peers pitch is the cheapest, fastest way to upgrade your own pitch. You’ll see exactly what’s working, what’s falling flat, and what investors actually lean forward for. The VC panel at the end is unusually candid because they’re talking to each other, not to a stage full of LPs. Register →

TTW 2026: Fundraising in 2026 — Panel & Mixer

The most explicitly fundraising-focused event on the entire calendar. Panel first, then a mixer.

Why you should be there: Go for the second half. The mixer is the highest concentration of founders-in-active-raises you’ll find all week, which means you’ll meet people in your exact stage and situation. Compare notes on which investors are responsive, which terms are reasonable, which intros are worth chasing. Founder-to-founder intel beats anything you’ll hear on the panel. Register →

9. Co-Founder Connect

An evening structured to help builders find or strengthen their founding teams.

Why you should be there: If you’re solo, or you have a skills gap on the team — usually technical when you’re business, business when you’re technical — this is worth six months of cold outreach. The format is built around matching complementary skills, not just networking, and the people who self-select into this room are serious about teaming up. Even if you don’t find your co-founder, you’ll find your first engineering hire or design partner. Register →

The Shortlist Founder Showcase: Toronto Edition (Presented by Boardy)

This is the talent room of the week. Six of Canada’s most consequential builders take the stage and each shares what they’re building plus the exact roles they’re hiring for right now. Then a curated mixer. Andrew Yeung and Boardy hand-pick the room: operators, builders, and executives who’ve actually shipped things.

Why you should be there: Two reasons. First, the operators in this room are exactly who you want as your first five hires — pre-vetted, job-curious, and the kind of people who pick a company based on the founder, not the comp band. Second, watching these outstanding founders pitch for talent is a masterclass in how to position your own company when you’re ready to recruit. Apply early, attendance is intentionally limited. Apply →

A Few Last Things

One piece of practical advice: register early. The best events fill up fast, the most popular ones already have waitlists, and others sold out weeks ago.

Once you’re in, two more things. The lobby usually beats the stage. Some of the best conversations of the week happen in the in-between moments, so leave room in your calendar for the unscheduled. And follow up within 48 hours, or it didn’t happen, everyone you meet is about to vanish back into their own busy week.

Tech Week pays off when you show up with a clear goal, not just a full calendar. Plan accordingly, and have fun out there.

About the Author

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When Amjad Masad pitched Replit a decade ago, one VC delivered what would become his favorite rejection. The investor told him the company was “a great thing for the world” — and that it should exist. He added that the same investor told him, “But I’m a capitalist and I don’t think it’ll ever make any money.”

Most founders would file that meeting away as a bad day. Masad filed it away as a recurring pattern. For the next eight years, Replit pursued a vision — that coding should be accessible to anyone with a browser, and that someday a billion people would build software — without a clear business model to defend it. Then, in roughly 12 months, the company went from $2.5M in ARR to $250M.

Speaking recently at Startup Grind, Masad walked through the long, contrarian arc that led there. For founders building anything ambitious right now, the more interesting story isn’t the hockey stick, it’s everything that came before it.

The setup-friction kid from Amman

The vision started, like a lot of good products, with personal annoyance. Growing up in Jordan, Masad’s father bought what he describes as “the first computer I’ve seen in my life”, and the first one in his neighborhood.

As a teenager, he turned it into a small business, building an Internet cafe management system for the local gaming shops. “It took me like two years to build it,” he said.

But every time he wanted to actually program something, the friction nearly killed the urge. “I would have to spend a lot of time downloading gigabytes worth of IDEs and dependencies,” he recalled. The fix was obvious to him, even then:

“You gotta be able to just open a browser window and start coding.”

That instinct stayed with him. By the time Replit was founded in 2016, the goal had grown well beyond convenience. It had become a thesis about scale: make coding easier, get more people building, and eventually you’d produce “a billion software creators.”

The desert years

The problem with a billion-creator vision is that it doesn’t look anything like a business in year three. Or year five. Or year seven.

“The hardest thing about it is having a vision and being in the desert and not really finding a business model for eight years,” Masad said. “It requires a lot of selling internally — not just VCs, not just inside the company.”

What kept the team aligned during those years wasn’t a deck or a forecast. It was user stories. Every month or two, someone would write in about how Replit had changed what they could build, or even what they did for a living. “We always have these individual solutions stories that would come up,” he said. That was the fuel.

The bet that broke the company, and then made it

In 2023, Masad stood on a TED stage and made a prediction that, even by Silicon Valley standards, was bold. AI agents were coming, he said, and they would solve software engineering end-to-end. Not autocomplete. Not snippet generation. The whole stack — write code, debug code, provision databases, deploy.

Internally, he committed to that future hard. “I felt comfortable to do that pivot, shut down all the other distractions that we’ve had,” he said. That meant layoffs. It also meant a confidence crisis: not everyone on the team believed the thesis. “We actually lost the full half of our team,” Masad said.

It is difficult to overstate how risky that period was. Replit had cut staff, alienated part of its team, and reoriented around a bet that the rest of the industry still treated as speculation.

Then Replit Agent shipped in September 2024 — the first coding agent on the market. ARR went from $2.5M to $250M in a year.

Coding agents aren’t really for coders

Once Replit Agent was in users’ hands, Masad noticed something he didn’t expect. The biggest unlocks weren’t happening to engineers. “If you’re not a coder, coding agents have actually more impact on your work than it has on engineers,” he said.

The examples he cited are the kind founders should be paying close attention to. A product manager at Zillow used Replit to build internal tools that, by his account, “increased offline revenue by tens of millions, perhaps $100 million.” A recruiting lead at another company started generating personalized candidate websites in minutes; response rates “double doubled.”

“You can become a 10x marketer, a 10x sales, by using tools,” Masad said. The framing he kept returning to: coding agents are general-purpose work tools that happen to produce code.

This has a direct implication for how founders should think about hiring. Masad’s take is blunt: “I don’t think raw IQ matters.” What matters now is agency — the ability to get things done and be resourceful with the tools available. “We see a power law,” he said. “We don’t see everyone adopting them.” A handful of operators on any team will absorb these tools and become an order of magnitude more productive. The rest won’t. Hire accordingly.

Lessons from being early on agents

Being first means making the mistakes first. Masad said Replit has already lived through the security and isolation problems competitors are only starting to encounter. “We’ve learned everything that people are learning now,” he said. “Like a year ago.”

The clearest rule he offered was unambiguous:

“Never connect a coding agent to a production environment. Never do that.”

There’s a quiet competitive moat in that experience. Replit’s full-stack architecture — agent, runtime, deployment, security — lets the company enforce guardrails users would otherwise have to assemble themselves. As one example, Masad noted, “we don’t allow the code engagement to install packages that are like a week old.” It’s the kind of opinionated default only an integrated platform can make.

He framed this as a new kind of product advantage:

“In a world where anyone can create anything, it’s more important than ever to create something with opinions and taste, and user experience and user safety in mind.”

– Amjad Masad

What founders should actually take from this

Three threads from Masad’s talk are worth holding onto.

1. Vision and speed aren’t opposites.

His phrasing: “be incredibly patient but moving incredibly fast in the near term.” Anchor your roadmap in five- and ten-year bets, but ship to customers weekly. The patient part is about what you’re building toward; the fast part is how you learn whether you’re right.

2. Distinguish the timeless from the trendy.

“Think about the things that are really core to humanity that are not going to change,” Masad said. Build for those. Use AI to get there faster — don’t build only for the AI moment itself.

3. The org chart is changing under everyone’s feet.

About 85% of the Fortune 500 already has Replit users somewhere inside the building. What varies, Masad said, is whether leadership knows or cares. The founders who win the next few years won’t be the ones who hired the most engineers. They’ll be the ones who built teams where every role — recruiting, sales, ops, product — operates with leverage that used to require an engineering ticket.

What the desert was for

Replit spent 8 years in the desert betting on that future. And the desert now is over.

The clearest sign of that came this March, when Masad introduced Replit Agent 4 — the company’s latest bet on where this is all going. His framing:

“Software isn’t merely technical work anymore. It’s creative. Introducing Replit Agent 4. The first AI built for creative collaboration between humans and agents. Design on an infinite canvas, work with your team, run parallel agents, and ship working apps, sites, slides and more.”

Software isn’t merely technical work anymore. It’s creative.

Introducing Replit Agent 4. The first AI built for creative collaboration between humans and agents.

Design on an infinite canvas, work with your team, run parallel agents, and ship working apps, sites, slides & more. pic.twitter.com/VCucf86wX6

— Amjad Masad (@amasad) March 11, 2026

That sentence — “software isn’t merely technical work anymore, it’s creative” — is the natural endpoint of the journey he described from the stage. The same instinct that started in a kid’s room in Amman, the one that said coding should be as simple as opening a browser, has compounded into a tool that lets anyone with an idea ship it.

The desert years were spent making that possible. The next chapter is what people do with it.

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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.

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I’m based in Toronto, but you can feel San Francisco from here. The mood spills into founder chats, hiring conversations, and every feed. Right now, the vibe feels hectic.

When OpenAI said it had closed a $122 billion round at an $852 billion post-money valuation, everyone noticed. When Anthropic’s $30 billion Series G put that company at a $380 billion valuation, the message got even louder. The upside in AI is massive. It is also packed into a very small circle.

Stanford HAI reported that private AI investment reached $344.7 billion in 2025, up 127.5% year over year. The same report shows California alone pulled in $218 billion. That kind of money changes behavior fast.

People outside that circle start doing ugly math. They look at their salary. Their options. Their runway. Their age. Then they look at a small group of people who made retirement money in a few years. A lot of them start asking the same question: am I already too late?

I care about that question because of its impact. It breeds fear and envy, causing smart people to abandon real problem-solving to chase big-name startups. This is the dark side of the AI boom: the money is real, but so is the crushing pressure.

The Gold Rush Is Warping How People See Work

For a long time, the Bay sold a simple story. Work hard. Be sharp. Join the right company. Stay close to the future. Your outcome might not be perfectly fair, but it could still make sense.

A gold rush breaks that story.

Suddenly the corporate ladder looks like the wrong building to climb. People start asking if they should become founders. Or switch jobs again. Or learn AI overnight. Or somehow get into the tiny group with life-changing upside.

Even the baseline is warped. Census data shows San Francisco County has a median household income of $140,970, a median home value of $1.39 million, and median gross rent of $2,476. In that environment, a very good income can start feeling average. That messes with your head if you let it.

This is where comparison gets dangerous. You stop asking, “What problem am I good at solving?” You start asking, “Which path gets me rich fastest?” That shift looks small. But it changes everything.

A lot of people now feel they can do all the right things, work a well-paying job for years, and still never touch those outcomes. Layoffs make that feeling even sharper. So does watching AI change the day-to-day shape of work almost overnight.

The Fear Is Real

The fear around work is not made up. Gallup’s 2026 survey found that 50% of U.S. employees now use AI in their role at least a few times a year. The same survey found that 18% believe their job is very or somewhat likely to be eliminated within five years because of AI or automation.

The emotional side looks rough too. Pew Research Center found that 52% of workers feel worried about workplace AI and 33% feel overwhelmed. Only 6% think it will create more job opportunities for them in the long run.

What does that do to a 24-year-old engineer? What does that do to a manager with kids? It makes work feel shaky at the exact moment people need stability.

A lot of engineers are wondering whether the skill they built their whole identity around still carries the same value. Younger workers feel this especially hard. A Stanford Digital Economy Lab paper found a 16% relative employment decline for workers ages 22 to 25 in the most AI-exposed occupations. You can see why so many younger people feel like the future moved without asking them.

Middle managers feel the pressure differently. Many have families. Many do not have deep AI skills. Many do not feel they can just wake up tomorrow and start a company. They can see the writing on the wall, but they do not see a clean path forward.

This is why I keep telling founders to stop thinking in black and white colors. The labor market is moving fast, but the answer is still messy. BLS projects software developer jobs to grow 15.8% through 2034, while data scientist roles grow 33.5% and computer programmer roles decline 6.0%.

Then look at how the tools behave. A Harvard Business School and BCG study found that GPT-4 users completed more tasks and finished faster when the task fit the model’s strength. The same study found accuracy dropped on a task outside that frontier.

AI helps. But AI also creates hidden challenges.

That grey area matters. If you panic, you either freeze or chase hype. Both are expensive.

Sudden Wealth Can Scramble Purpose Too

There is another side to this story that people do not talk about enough. Some people “made it” so fast that their old life plan stopped making sense.

I am not asking anyone to feel sorry for them. I am saying purpose gets weird when money arrives before meaning catches up. If work was your path to freedom and now you are already there at 30, what comes next? For some people, the answer becomes status, attention, or building another company just because they can.

You can feel that emptiness in the ecosystem too. Some companies are being built from obsession. Some are being built from envy. Some are being built for status points. The market eventually feels the difference.

I do not believe early-stage founders get a clean work-life balance. There is more like work-life blend. Your brain is 100% on. That only works when the problem matters to you so much that you turn your work into play. If the mission is fake, the grind gets very dark very fast.

The dark side of entrepreneurship has always been there. AI money just made it louder.

The Media Machine Is Feeding the Wrong Lesson

Traditional tech media keeps pushing the same signals to the top. Big rounds. Big valuations. Big names. That trains everyone to believe only the already visible companies matter.

I believe this is a major gap in the startup world.

The founders who need visibility most are usually the ones who get ignored. The bootstrapped founder who will never get a TechCrunch headline may be solving a sharper problem than the loudest company in the room. They may understand their customer better too.

That gap is exactly why I built BYVI. My team and I went through a period when getting traction and exposure felt really hard. Then a journalist came out of nowhere and wrote about us. That story changed our path. It led to partnerships and clients. I never forgot that feeling.

It made something very clear to me. PR matters most when you are early. Later, press can be a nice signal. Early on, it can change the company.

What Founders Should Do Now

Build from Proximity, Not Panic

If AI has you feeling late, slow down. Panic is expensive.

I gave this advice to a founder once, and I would give it again today. Build in the industry you already understand. A founder friend of mine wanted to jump into a space that had nothing to do with his background. He had 10 years of experience elsewhere. My advice was simple: stay close to what you know, solve a problem for people you understand, and use the expertise you already paid for with your life.

That advice matters even more in a hype cycle. Go by what’s already happening under the hood. What problem do you understand deeply? Which customer do you actually know? Where do you already have trust, context, and language?

Across BriefBid, DeckLinks, and BYVI, I keep coming back to the same pattern. I saw a gap. Then I built something practical around it. I did not start with the trend. I focused on the problem and the people I cared about most.

If you are still employed, you do not need to blow up your whole life tomorrow. Learn while you are inside the system. Talk to buyers. Notice repeated pain. Sometimes the best startup idea is sitting inside the workflow that currently frustrates you.

Own Your Own Content Engine

The AI boom changed distribution too. Visibility now comes from many places at once. Google. AI search. Reddit. Reviews. Product docs. Niche blogs. Newsletters. Founder posts. Your brand story gets built across all of them.

AI search does not go by keywords the old way. Buyers now explain their whole situation, almost the same way they do on a sales call. Your content needs to answer that directly.

That is why I feel very strongly about this point: own your own content engine. The true authority cannot be outsourced. Bring the content writing in-house. Or at least keep the raw knowledge in-house and turn it into content with care.

Don’t forget that LLMs are pattern matching machines. They look for consistency. Depth. Proof. They crawl Reddit for community sentiment. They crawl reviews. They read case studies. They read documentation. If your website says one thing, your founder says another, and your customers say something else, the model gets confused. Then it forms the wrong narrative about you.

This is where founder-led content gets very powerful. Edelman and LinkedIn found that 53% of decision-makers say high-quality thought leadership makes brand recognition matter less, and 79% of hidden decision-makers are more likely to advocate for an RFP from companies that publish it consistently.

At DeckLinks, I have spent a lot of time thinking about how B2B content feels on the receiving end. Human-centered content still wins attention. In the AI era, it wins even more. Buyers want a human behind your brand.

What Investors, Advisors, and Ecosystem Builders Need to Change

This part is not only on founders. Investors, advisors, journalists, and community builders shape the mood too.

Stop rewarding headline theater. Look for the founders who are quietly solving boring, painful problems.

If you really want to support early-stage founders, do more than applaud them online. Buy from them when there is a fit. Pilot the product. Make the intro. Put them in the room with the right buyer.

Founder communities cannot live on encouragement alone. Founders need customers. Partners. Momentum. Otherwise the community just turns into a pointless social club.

I also think communities should be highly curated. Verticalize the events. Keep the room relevant. Founders open up more when they feel they are among peers who understand the same reality. That’s when the conversation gets honest. And that’s when people stop performing success.

The ecosystem learns faster when people close to the problem speak openly. Founder-to-founder stories do that better than polished PR ever will.

Final Thought

SF’s AI gold rush is changing wealth, work, identity, and ambition all at once. That is why it feels so intense. It reaches far beyond a tech trend. It is reshaping how people measure themselves.

I still think this is one of the best times to build something new. New technology creates new pain points, and pain points create companies. But you need the right reason to build.

Solve the problem you know. Stay close to your niche. Own your own content engine.

I hope founders do not give up, no matter how loud this market gets. Most founder stories are not straight lines. They are hustle, grind and endless pivots. Keep going anyway. Keep iterating. You are going to get somewhere.

And if you have influence in this ecosystem, use it to make visibility and opportunity accessible to the people who actually need it.

FAQs

How is the AI boom impacting junior engineering roles at early-stage startups?

It is actively shrinking entry-level opportunities. A Stanford study found early-career workers in highly AI-exposed roles faced a 16% relative employment decline. Startups are leaning on models to execute junior code, forcing new hires to demonstrate senior-level problem solving immediately to survive the current market.

Does the AI capital boom make building a startup outside Silicon Valley harder?

Capital is aggressively localized, but your customer base is not. In 2025, California absorbed $218 billion of US private AI investment. While funding is intensely concentrated, founders outside SF can leverage lower burn rates to build highly profitable, bootstrapped businesses without the extreme local psychological pressure.

Can generative AI genuinely bridge the resource gap for bootstrapped founders?

Absolutely, especially when scaling basic operations. Research indicates that generative AI assistants increased customer support productivity by 14% on average, heavily benefiting less-experienced workers. Bootstrapped founders can leverage this to build incredibly lean teams, accelerating execution without raising VC money just to cover overhead.

Should non-AI startups force generative AI features into their MVP to attract investors?

Absolutely not. Forcing AI into an MVP signals panic, and panic is expensive. While generative AI companies pulled in $170.9 billion in 2025, sophisticated ecosystem builders see right through thin wrappers. Solve your core customer’s actual pain point. Do not build for the hype cycle.

About the Author

The post The Dark Side of AI Gold Rush: Why Everyone Feels Behind appeared first on BYVI.

My team and I went through a period when traction felt painfully hard. We were building. We were trying. Very few people saw us. Then a journalist came out of nowhere and wrote about us. That one story brought partnerships and clients. It also gave us the push to keep going.

That experience changed how I think about visibility. It is one of the reasons I built BYVI. I wanted to make press coverage accessible to all startups regardless of their funding stage, especially the early-stage and bootstrapped founders who usually get ignored until they raise money.

Now I see a similar opening with AI search.

Your advantage as a bootstrapped founder is your deep, practical understanding of your niche. A lot of you built the solution for the problem you experienced firsthand. And that first hand expertise is a major asset for AI search.

AI Search Changed How B2B Buyers Ask for Help

B2B buyers do not search the way they used to. They are not typing one neat keyword and hoping for the best. They explain the whole situation.

This was clear when I helped a friend who runs a recruiting agency write an article about hiring software developers. We targeted early-stage founders specifically, rather than general hiring managers, and I included a section dedicated entirely to hiring a founding engineer.

It reached the first page of Google in just a couple of days. Despite the search query being incredibly competitive, AI summaries picked it as a top source, putting us right next to YC and YouTube.

Soon after, my friend called to say a lead had come in referred directly by ChatGPT. The AI had suggested Linkus Group to someone looking for a founding engineer. This shows that when your content speaks directly to your ideal client, you can train AI to recognize your value and recommend you as the solution.

That is why old keyword thinking feels too small now. Winning content speaks to a real person in a real moment, reflecting the exact language of your ICP. It’s like sitting beside them and saying, ‘Hey, I get the mess you’re in – let me help.’

For bootstrappers, this matters even more because the upside of organic search is so big. Data-Mania reports B2B SEO can deliver around 748% ROI, while paid search sits around 36%. BrightEdge data points to 2× more revenue from organic SEO than any other channel for B2B companies. When cash is tight, that kind of return matters.

The Old SEO Playbook Is Quietly Hurting B2B Brands

A lot of founders are still getting old advice. Hire an SEO agency. Pick keywords. Publish as much as possible. Hope something ranks.

I think that playbook is breaking fast.

But the bigger problem is trust. Content is your reputation on the line. If a buyer lands on your site and reads AI slop of fluff, they will feel it right away. People can see through generic fluff. Buyers are tired of it, and AI search is getting better at filtering it out.

Google can also react badly when a site gets flooded with generic AI content. Pages can struggle to get indexed. That is a dangerous move for bootstrapped startups that cannot afford to have their domain flagged.

Velocity still matters, of course. Publishing 16 or more blog posts a month has been linked to 4.5× more leads. But quality decides whether that velocity helps you or hurts you. Better to be thoughtful and insightful than post fluff every day.

Bootstrappers Have the Raw Material and Speed AI Wants

This is why I believe bootstrappers will win.

You are usually closer to the pain than a larger company is. You are on the sales calls. You are handling objections. You are reading support messages. You are hearing the same concerns again and again. You can hear what people are saying, and if you pay attention, you can hear what they’re not saying too. Reading between those lines often comes with experience.

The Power of Industry Vocabulary

Since AI looks for depth, founders should lead with their firsthand experience and niche expertise to signal authority. The founder with 10 years of real industry knowledge has a major edge. They use the right words, what’s often called industry vocabulary. They understand the real objections. And they can convert all this firsthand industry knowledge into very impactful content.

The Bootstrapper Speed Advantage

And then there is speed.

A big company often needs rounds of approval before a piece goes live. A bootstrap founder can publish while the pain is still fresh. If you live in a work-life blend, and most founders do, your brain is already connecting the dots all day. Use that.

• After a call, write down the objection.
• After a customer question, turn the answer into a page.
• After a demo, capture the explanation that made the buyer lean in.

AI search favors that kind of freshness when it is paired with real expertise. I have seen clean, focused content get picked up by LLMs within a couple of days when the formatting is simple and the answer is easy to find.

How to Format Content for AI Discoverability

When I say formatting, I don’t mean H2s and H3s.

AI Rewards Proof, Consistency, and Human Detail

AI Search Evaluates B2B Brands Beyond Your Website

One of the biggest mistakes I see is founders thinking AI search only looks at their website.

It pulls from much more than that.

LLMs love Reddit. They crawl for community sentiment. They pull from G2, review platforms, testimonials, niche publications, product documentation, help centers, and public conversations around your category. They want proof. They want context. They want signs that real people trust you.

My founder friend acquired clients because AI search models cited his helpful Reddit comments. Those comments compared the solution to competitors in a neutral, useful way. That says a lot about where authority comes from now.

Leveraging Case Studies to Influence AI Search Results

Your case studies matter here too. AI looks for proof, and case studies are a strong signal. When you write one, describe the client in detail. Talk about their stage, their problem, and the situation they were in. That context helps AI connect your solution with a similar buyer later.

Why Inconsistent Content Hurts AI Search Visibility

Because AI synthesizes your entire digital footprint, message consistency is non-negotiable.

This is another reason I like bootstrappers in this market. Smaller teams can stay aligned more easily. There are fewer layers. There is less polishing. There is more truth.

Build AI Search Authority Through Niche Publications

In traditional SEO, boosting your website’s authority is often as simple as securing backlinks from sites with a high Domain Rating (DR), and historically, search engine algorithms haven’t cared whether you earned that link organically or paid for it

AI search models, however, are fundamentally different. They prioritize genuine, contextual trust signals from independent publications rather than just looking at link metrics. Because of this, authentic earned editorial coverage is far more effective for boosting your AI search visibility than paid press releases or sponsored links.

However, not all earned media serves the same purpose. While mentions in mainstream outlets like TechCrunch are great for attracting investors, they aren’t always the best path to customer acquisition.

Own Your Own Content Engine

If you are a B2B founder, regardless of whether you are bootstrapped, you should bring content writing in-house and own your own content engine.

You can absolutely get outside help with setting up the right process, this is where I’d come in. But the insights and knowledge source has to come from inside the company.

Pull from sales calls. Pull from closed-lost calls. Pull from support tickets. Pull from FAQ threads. Pull from onboarding friction. Pull from product docs. Pull from your co-founders and your internal experts. You get the idea.

Publish Content Where AI Crawlers Already Are

While your website still matters, because traditional Google indexing acts as a major gateway into AI search, your strategy must extend to the the platforms that AI algorithms crawl.

I’m a huge fan of LinkedIn. I love that its long-form articles often surface in AI search results to reach decision-makers directly. Surprisingly, even articles with very few likes appear in AI overviews and get cited by LLMs, which means AI doesn’t prioritize virality.

And I also see YouTube appearing frequently in AI overviews. I believe now is the ideal time for founders and their teams to start creating video content to increase brand visibility and shape their own narrative.

Final Thoughts

This is one of the best times for bootstrapped B2B founders to win attention.

Bootstrapped startups rarely have the massive budgets needed to buy growth, hire expensive sales teams, or secure headlines in major publications. But AI search has opened an entirely new lane. Today, helpful, specific, and deeply human content can put you directly in front of buyers without a massive spend.

The key is staying consistent long enough for your efforts to compound, which is exactly where most founders give up. AI models are constantly learning from what they find. Every useful page, thoughtful comment, detailed case study, and niche collaboration adds another trust signal. Over time, those signals synthesize into your brand’s truth.

So keep going my dear bootstrapped fellows. Channel your frontline experience into content that offers real depth and proof. Share the exact details your buyers actually need. Always stay close to your customers and actively control your narrative before someone else does.

If you pair your real-world experience with authentic proof, you will become impossible for AI to ignore. And once that flywheel starts spinning, the exact right clients will find you at exactly the right time.

FAQs

How can a lean bootstrapped team maintain the content velocity required for AI search?

You do not need a massive team. You need a ruthless system. Repurpose everything you create. Publishing 16 or more content pieces monthly drives roughly 4.5x more leads. Hit that volume by slicing one deep founder insight into multiple formats to survive.

Should bootstrapped startups abandon paid search entirely in favor of AI-driven B2B SEO?

Yes, especially early on. Paid search is a brutal game against VC-funded giants. B2B SEO delivers a massive 748% ROI compared to just 36% for paid search. Build your organic engine first. Buying ads will burn through your cash too quickly.

How does LinkedIn factor into an AI search strategy for early-stage B2B brands?

AI models constantly scrape social platforms for brand sentiment. LinkedIn is a goldmine for this. With an impressive visitor-to-lead conversion rate of about 2.74%, your organic posts feed the crawlers while directly converting the actual decision-makers already in your network.

Does AI search make traditional B2B email marketing obsolete for bootstrapped founders?

Not at all. AI search brings them to your doorstep, but email keeps them in your ecosystem. Email remains highly efficient, delivering around 261% ROI. Capture high-intent traffic with search, then convert these leads into email subscribers for nurturing.

About the author

The post AI Search for B2B Brands: Why Bootstrappers Will Win appeared first on BYVI.