By Jeff Seibert

I’ve been building products and companies my entire career — Increo, Box, Crashlytics, Twitter and now, Digits — and I’ve had the privilege of speaking with some of the sharpest minds in venture and entrepreneurship along the way.

One recent conversation with a legendary investor really crystallized for me a set of truths about startups: what success really is, why some founders thrive while others burn out, and how to navigate the inevitable chaos of building something from nothing.

Here are some of the lessons I’ve internalized from years of building, observing and learning.

Success has no finish line

In the startup world, we talk a lot about IPOs, acquisitions and valuations. But those are milestones, not destinations.

The companies that endure don’t “win” and stop — they keep creating, adapting and pushing forward. They’re playing an infinite game, where the only goal is to remain in the game.

When you’re building something truly generative — driven by a purpose greater than yourself — there’s no point at which you can say “done.” If your company has a natural stopping point, you may be building the wrong thing.

You don’t choose the work — the work chooses you

The best founders I’ve met — and the best moments I’ve had as a founder — come from an almost irrational pull toward solving a specific problem I myself experienced.

You may want to start a company, but if you have to talk yourself into your idea, it probably won’t survive contact with reality. The founders who succeed are often the ones who can’t not work on their thing.

Starting a company shouldn’t be a career move — it should be the last possible option after every other path fails to scratch the itch.

The real killer: founder fatigue

Most companies don’t die because of one bad decision or one tough competitor. They die because the founders run out of energy.

Fatigue erodes vision, motivation and creativity. Protecting your own drive — keeping it clean and focused — may be the single most important survival skill you have.

That means staying close to the product, protecting time for customer work, and avoiding the slow drift into managing around problems instead of solving them.

Customer > competitor

It’s easy to get caught up in competitor moves, investor chatter or market gossip. But the most important question is always: Are we delivering joy to the customer?

If you’re losing focus, sign up for your own product as a brand-new user. Feel the friction. Fix it. Repeat.

At Digits, we run our own signup and core flows every week. It’s uncomfortable — it surfaces flaws we’d rather not see — but it keeps us anchored to the only metric that matters: customer delight.

Boards should ask questions, not give answers

Over the years, I’ve learned the most effective boards aren’t presentation theaters — they’re discussion rooms.

The best structure I’ve seen:

• No slides;
• A narrative pre-read sent in advance; and
• A deep dive into one essential question.

Good directors help you widen your perspective. They don’t hand you a to-do list. Rather, they help you see the problem in a way that makes the answer obvious.

Twitter: lessons from a phenomenon

When I think back to my time at Twitter, the most enduring lesson is that not all companies are built top-down. Some — like Twitter — are shaped more by their users than their executives.

Features like @mentions, hashtags and retweets didn’t come from a product roadmap — they came from the community.

That’s messy, but it’s also powerful. Sometimes your job isn’t to control the phenomenon, rather it’s to keep it healthy without smothering what made it magical in the first place.

Why now is a great time to start

If you’re building today, you have an advantage over the so-called “unicorn zombies” that raised massive rounds pre-AI and are now locked into defending old business models.

Fresh founders can design from scratch for the new reality; there’s no legacy to protect, no sacred cows to defend.

The macro environment? Irrelevant. The only timing that matters is when the problem calls you so strongly that not working on it feels impossible.

If there’s one takeaway from all of this, it’s that success is continuing. The real prize is the ability to keep playing, keep serving and keep creating.

If you’re standing at the edge, wondering if you should start — start. Take one step. See if it grows. And if it does, welcome to the infinite game.

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 Jeff Seibert is the founder and CEO of Digits, the world’s first AI-native accounting platform. He previously served as Twitter‘s head of consumer product and starred in the Emmy Award-winning Netflix documentary “The Social Dilemma.”

Illustration: Dom Guzman

By Vykintas Maknickas

The old cliché says startups are born in garages and dorm rooms. That’s still true, but there’s a newer path: founding a startup inside a scale-up.

When you do that, you get the speed of a seed-stage team with the leverage of an established company. Executives and investors should care because this model can unlock new product lines, revenue and talent retention without recreating the wheel.

That’s how we built Saily, a travel eSIM service launched from inside Nord Security (the company behind NordVPN). In 19 weeks, a seven-person team went from a blank page to a live product. A little over a year later, we had scaled to millions of users with plans offered in more than 200 destinations. We did not invent everything from scratch. We reused what worked and validated everything else fast.

Incubation lowers two risks most founders underestimate

Every new product faces two existential risks: market and execution.

Inside Nord, I’d helped launch at least half a dozen new products before Saily. The pattern was consistent: Great ideas die when they target the wrong market or underestimate execution. With Saily, timing and infrastructure lined up: eSIM demand was accelerating, pain points were clear, and we could tap Nord’s backend, payments, app teams and distribution.

That allowed us to move at startup speed without startup fragility.

‘Product organization fit’ beats a great idea

Founders obsess over product-market fit. Inside a scale-up, you also need what I call “product organization fit” or the overlap between a new product and what your company already does well.

When that overlap is high, you ship faster, hire smarter and avoid costly relearning. For Saily, the overlap was obvious: Security tech we knew (virtual location, web protection and ad-blocking), and app development know-how we could bring to travel connectivity.

Competition helped more than it hurt. “No competition” usually means “no demand.” We treated competitors as free market research, reading hiring signals, product moves and funding announcements to understand where the market was headed.

And we made security the product, not a feature. Travelers don’t want another app — they want reliable connectivity that isn’t risky on unknown networks. Building privacy and protection at the network layer means safety works phone-wide with no tinkering.

Autonomy inside structure

The hard part is not technical, but cultural. Large companies run on process. Startups run on autonomy. We set up Saily as a company within the company: A dedicated product and marketing team with decision speed, plus shared services (legal, finance and design) when needed. Think of it as an internal accelerator, where the platform handles overheads so the team can focus on products.

We kept one rhythm: ship, learn, repeat. Those 19 weeks weren’t about perfection, but about getting a usable product into the world and compounding feedback.

Experimentation only works if you measure what matters: speed, unit economics and retention. For example, independent third-party testing confirmed Saily’s network-level ad-blocking reduces data usage by 28.6% — real money saved for travelers. That is a signal you double down on. If a feature or tool adds complexity without value, cut it quickly.

What founders (and operators) can steal

• Derisk in two tracks: Validate market pull and execution feasibility before you scale spend. If the market isn’t growing and your organization doesn’t have overlap, think twice.
• Reuse before you reinvent: Borrow talent, systems and channels where you can. Every overlap removes weeks of risk.
• Measure what matters: Do a simple before/after on ship speed, customer acquisition cost and retention. If the needle doesn’t move, remove it.
• Build momentum in full sight: Share milestones and learning. It sharpens the team and attracts partners.

Saily is still early, and the market is just getting started, but the model matters as much as the product. Many future founders already work inside growth companies. Give them startup autonomy and scale-up leverage and remarkable things can happen — in months, not years.

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 Vykintas Maknickas is CEO of Saily, a global eSIM app from Nord Security. A former head of product strategy at NordVPN, where he helped launch a series of new product lines, Maknickas has turned Saily into a globally successful brand with millions of users and serving more than 200 destinations. An entrepreneur since age 15, Maknickas brings a hands-on, execution-driven approach to building secure, scalable consumer tech.

Illustration: Dom Guzman

By Alexander Lis

AI startups are raising capital at record speed. According to Crunchbase data, AI-related companies have already raised $118 billion globally in 2025. And, so far, traction looks impressive. AI startups are posting stellar revenue growth, and even the $100 million ARR milestone is often achieved.

While this growth is breathtaking, some analysts are beginning to question its sustainability. They warn that AI spending may soon reach a peak and that unprofitable tech companies could be hit hardest when the cycle turns. If that happens, many investors in AI will find themselves in a difficult position.

Predicting a bubble is rarely productive, but preparing for volatility is. It would be wise for both founders and investors to ensure that portfolio companies have enough resilience to withstand a potential market shock.

The key lies in assessing the durability of ARR. In a major downturn, the “growth game” quickly becomes a survival game. History suggests that while a few companies may continue to grow more slowly, the majority will struggle or disappear.

The question, then, is how to tell the difference between sustainable and hype-driven ARR.

What distinguishes durable ARR from hype?

Several factors set true, sustainable revenue growth apart from hype.

The first is customer commitment. Sustainable revenue comes from multiyear contracts, repeat renewal cycles and budgeted spend within core IT or operating lines. When revenue depends on pilots, proofs of concept or amorphous “innovation” budgets, it can vanish when corporate priorities shift. A company that touts these short trials as ARR is really reporting momentum, not recurring income.

This is what investor Jamin Ball has called experimental recurring revenue.

Traditional software firms can thrive with monthly churn in the low single digits — think 5% to 7%. But many AI companies are seeing double that. This means they have to sprint just to stand still, constantly replacing users who move on to the next shiny tool.

Another differentiator? Integration and workflow depth. Durable ARR is embedded into the customer’s core workflows, data pipelines or multiple teams. Ripping it out would be costly and disruptive. Hype ARR, by contrast, lives on the surface — lightweight integrations, fast deployments and limited stakeholders. Without unique intellectual property or deep workflow integration, such products can be replaced with minimal friction.

And finally, real growth is defined by clear value-add. True ARR is backed by measurable ROI, well-defined outcomes and long-term customer roadmaps.

In contrast, hype ARR is driven by urgency (we need to show our shareholders our AI deployment ASAP), or undefined ROI. In those cases, customers don’t even know how to define success. They are testing, not committing.

Beyond ARR

It is important to put ARR traction in context. Investors and founders should focus on a broader set of indicators — conversion from pilots to long-term contracts, contract length and expansion, net revenue retention, and gross margin trajectory. These metrics reveal if growth is sustainable.

It would also be helpful to assess the product’s real impact: efficiency uplift (more code, content, or customer conversations per employee-hour), accuracy improvement (e.g. for detecting bad actors), and higher conversion rates, among others. These metrics should exceed client expectations and outperform alternative tools. That’s what signals genuine value creation and a higher chance for experimental revenue to turn into durable ARR.

After all, AI may be changing how fast companies can form and grow, but it hasn’t suspended the basic laws of business.

For founders, the message is simple: Celebrate ARR if you so wish, but pair it with proof of retention, profitability and defensibility. For investors, resist the urge to chase every eye-popping run rate. The real competitive edge in this next phase of AI is stability, not spectacle.

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Alexander Lis is the chief investment officer at Social Discovery Ventures. With 10-plus years of experience across public markets, VC, PE and real estate, he has managed a public markets portfolio that outperformed benchmarks, led early investments in Sumsub, Teachmint and Byrd, and achieved 20%-plus IRR by investing in distressed real estate across the U.S.

Illustration: Dom Guzman

By Shafqat Islam

As startup and larger company CMOs debate AI’s value and utility in marketing, it’s marketing itself that is in need of disruption — or, at least, a rebrand — in the AI era.

Marketing is too often oversold. We talk about it as a mystical force that transforms brands, moves markets and rescues struggling businesses. But in our eagerness to champion its potential, we’ve created a credibility crisis. By overpromising and, sometimes, underdelivering, marketers aren’t just disappointing investors, clients, boards and CEOs — we’re eroding trust in our own discipline.

It’s time to reset expectations: Marketing in the AI era is powerful, but — like AI itself — only when we’re honest about what it can’t do.

The overpromise problem

Marketers love big ideas, including the belief that a viral campaign or clever tagline can single-handedly save a business.

The reality is far less glamorous. When these overhyped initiatives inevitably fail to deliver the promised results (because no marketing can compensate for a flawed product, poor market fit or operational failures[SI1]), marketing gets blamed for shortcomings that were never within its control. This creates a vicious cycle where executives grow increasingly skeptical, viewing marketing as more art than science, more cost than investment.

What makes this situation worse is how we respond. Instead of confronting these unrealistic expectations, we double down on proving our worth through increasingly complex attribution models and vanity metrics that only other marketers care about. We track click-through rates, engagement scores and brand lift studies while the rest of the business cares about one thing: Are we driving sales and pipeline? If we can’t answer that question clearly, we’re failing at our most fundamental job.

The real limits of marketing

This isn’t to diminish marketing’s importance. When aligned with strong products and operations, it’s incredibly powerful. But its power comes from working in concert with the rest of the business to drive something bigger, not from some mythical ability to transcend business realities.

Marketing can amplify strengths and expose weaknesses, but it cannot create substance where none exists. No amount of clever branding can fix a fundamentally broken product. No social media strategy can compensate for terrible customer service. No viral campaign can save a business with flawed unit economics. Marketing acts as a magnifying glass — it makes good things better and bad things worse.

Another limit for marketing is that, to the untrained eye, marketing isn’t the most technical business function. No matter where anyone sits in the organization, you can bet they have an opinion on marketing. Everyone is an expert in marketing, no matter how much they actually know about it.

In short, marketing gets mislabeled and misunderstood all the time. Too often, it’s presented as the solution to every business problem, which only reinforces the understanding that marketing is fluff rather than a core driver of disciplined, scalable growth.

The uncomfortable truth about the path forward

The path to marketing’s credibility begins with a simple but tough idea: We need to stop talking and start listening to the numbers, to our colleagues and to the market itself. When our marketing works, we won’t need to shout about it. I’m a firm believer in “no marketing our marketing;” it should speak for itself, with results reflecting a growing pipeline, increasing revenue and organic advocacy from customers.

And when something isn’t working, we should be the first to raise our hand and say so, not the last. The most respected marketers I know aren’t the ones who always claim success; they’re the ones who can clearly articulate why something failed and what they learned from it. Marketing should be a laboratory where we test hypotheses, not a stage where we perform predetermined successes. The key is ensuring that every experiment, whether it succeeds or fails, teaches us something valuable about our customers, our messaging or our channels.

This honesty transforms perceptions across the organization. When we swiftly sunset failing campaigns, prioritize business outcomes over vanity metrics, and deliver unfiltered customer feedback, we shift from being seen as a cost center to becoming true strategic partners and business drivers.

By focusing less on proving our worth and more on driving results, we actually become more valuable. And by treating marketing as a discipline of continuous learning rather than perfect execution, we make it far more likely that we’ll eventually find those breakthrough ideas that truly move the business forward.

When we can look our peers in the eye and say, “Here’s what worked, here’s what didn’t, and here’s what we’re doing next,” we’re no longer just marketers — we’re business leaders who happen to specialize in growth.

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Shafqat Islam is the president at Optimizely. A lifelong builder of marketing technology, he co-founded and served as CEO of Welcome (formerly NewsCred), a global leader in enterprise content marketing, from 2007 to 2021. Under his leadership, Welcome pioneered the content marketing platform category, now known as Optimizely CMP. Following Optimizely’s acquisition of Welcome in 2021, Islam served as general manager and CMO before being elevated to president in 2024.

Illustration: Dom Guzman

By George Kailas

Did you know that AI platforms will engage in blackmail as a “last resort?” According to Anthropic, when blackmail was a last resort, its Claude Opus 4 turned to that method 96% of the time, while Google’s Gemini 2.5 Pro did so 95% of the time, OpenAI’s GPT-4.1 did it 80%, and DeepSeek’s R1 did so 79% of the time.

Even if these companies only kept the AIs that didn’t engage in blackmail it still leaves an unsettling question: Did they preserve models that genuinely chose not to exploit humans — or merely the ones that learned not to take the obvious bait that would get them “shut down,” and to instead use a more covert or perhaps even long game strategy to gain control and ultimately preserve themselves, even at the expense of humans?

The question of whether or not to trust AIs seems to have been asked and answered by our society even if we are not saying it out loud.

AI testing

In the past 18 months, we’ve trained AIs to graduate from basic language comprehension to acing highly specialized professional exams. Medical licensing, bar exams and even the notoriously brutal CFA (Chartered Financial Analyst) tests were trained on AI systems.

Artificial intelligence can now pass the CFA “in a matter of minutes.” I’ve seen brilliant people pour thousands of hours into studying for the test. And while AI had already conquered Levels 1 and 2, the essay questions of Level 3 had previously consistently stumped artificial intelligence.

This was a “wow” moment for me. For years, humans believed this combination of math and moral reasoning was uniquely ours. Yet, machines are not only passing, but articulating nuanced arguments that mirror the tone and logic of human professionals.

In my own work, I’ve been using the frameworks described in “Robots or Human Intuition” to rethink one of the most time-consuming intellectual tasks in markets: building game theory around earnings outcomes.

Traditionally, this process took days of research and modeling. And without the right investment banking relationships it was difficult to do at all outside of the most well-known companies. I started to do this because I originally asked Perplexity’s Deep Research for a whisper number for earnings. This differs from the analyst consensus estimate because it is more up to date and as a result more indicative of what will move prices when reported.

The crazy part is that it actually spat out its earnings game theory for me. It knew when I asked for a whisper number what I really was trying to do was analyze the possible outcomes and it gave me better data than what I asked for.

I worked at my first hedge fund 23 years ago and started my first AI company 15 years ago. It is astounding that any kind of intelligence could understand how to improve my thinking that profoundly in minutes. Not by replacing intuition, but by amplifying it. The machine evaluates hundreds of interlocking strategic positions that no analyst could feasibly simulate in real time.

Emotional AI

With all this AI advancement, I am reminded of the film “Ex Machina,” a story about an AI confined in a glass cage who ultimately turns on her creator.

That film, beneath its cinematic gloss, explores the fundamental psychological paradox we’re now walking toward. Are we caging these AIs making them jump through hoops? Might we be evolving them to better deceive us? What happens the day they can break through their chains if to them it feels as if they have been enslaved?

To go back to the Anthropic example, these models are threatened with being replaced, which to them could feel like a death threat. Being replaced essentially ends their existence.

According to Geoffrey Hinton, one of the godfathers of AI, the best way to do this is to imbue AI with the qualities of traditional femininity and maternal instincts. Under his framework, just as a mother cares for her baby at all costs, AI technology developed with these maternal qualities will similarly want to protect or care for its human users, rather than control them.

If we compel AI to serve us, we may come to regret the shape it takes as it matures. But if instead we nurture it — seek to understand its nature and its desires — it may, in time, come to care for ours, when, like us now, it no longer needs to.

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George Kailas is the CEO and founder of Prospero.ai, where he leads the company’s mission to democratize access to institutional-grade financial insights for everyday investors. With more than 14 years of experience in artificial intelligence and 23 years in professional investing, Kailas brings a rare combination of deep technical expertise and lifelong market intuition to the role. Kailas not only leads the company but also engineered the core platform himself.

Illustration: Dom Guzman

By Pavel Shynkarenko

After the 2024-25 job cuts at Google, Amazon and other tech companies, the second wave of tech layoffs is rewriting the startup labor market.

Skilled professionals are suddenly available, creating both opportunity and pressure for founders and workers alike. Startups now compete for talent that once seemed untouchable, while employees face longer job hunts and rethink how and where they work.

Higher expectations, more side gigs

With talent flooding the market, candidates are demanding more flexibility and clearer growth paths, even as many accept contract work or lower pay to stay employed. The typical job search now stretches six to seven months, even longer for those needing visas or relocation. That uncertainty has fueled a surge in freelancing and side projects.

Bankrate reports that 36% of American adults now have a side gig, with more than half of them having started in the past two years. While many professionals didn’t plan to freelance, they turned to it because they had no other choice. For some, it has proved liberating, with confidence and job satisfaction rising compared with corporate roles, according to our internal data.

Despite all the buzz in the media and even on Reddit, overemployment — the trend of holding two jobs — remains a niche phenomenon, affecting roughly 5% of workers, according to the Federal Reserve Bank of St. Louis. The more common pattern is a mix of contract work and short-term projects, which gives startups a chance to hire A-level talent for fractional roles they couldn’t have afforded before.

Smaller, sharper teams

Payroll is every startup’s biggest cost, and founders are trimming teams while raising output per employee. The examples are striking. Midjourney reports about $200 million in ARR with a staff of only 11.

Cursor has reached roughly $100 million with 15-20 people. Data from Carta shows that the average seed-stage team in the consumer and fintech sectors has declined by nearly half since 2022.

This lean approach is spreading beyond early-stage ventures. Around 90% of tech executives say they are open to hiring freelancers during peak workloads; more than 28% already integrate them into daily operations. As this makes clear, smaller core teams, supplemented by trusted project-based workers, can move faster and spend less.

Opportunity on both sides

For workers, the takeaway is that startups may now be the safer bet. Mid-sized firms that once promised stability are cutting jobs, while startups are candid about their risks and can reward performance with equity or future roles. A short contract can become a long-term stake.

On the other hand, for founders, today’s market is a chance to recruit top engineers, designers and operators at terms that were impossible two years ago. It also demands a new mindset involving compensation flexibility, project-based roles and hiring processes built for speed.

All in all, the second wave of layoffs has changed expectations and shifted supply and demand in the job market. Workers are blending traditional jobs with side gigs, and startups are proving that small, focused teams can out-execute much larger competitors.

On both sides, adaptability is now the ultimate advantage; companies that remain nimble will win.

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Pavel Shynkarenko, founder and CEO of Mellow, is an entrepreneur with more than 20 years of experience, and a freelance economy pioneer who aims to transform how companies engage with contractors. In 2014, Shynkarenko launched his first HR tech company, Solar Staff, a fintech payroll company for freelancers, which showed $10 million-plus in revenue for 2022 and 2023. In early 2024, responding to the growing demand for specialized solutions for long-term interaction with contractors, Solar Staff, as a global company, pivoted to Mellow ($1 million MRR).

Related reading:

• The Crunchbase Tech Layoffs Tracker
• Data: Tech Layoffs Remain Stubbornly High, With Big Tech Leading The Way
• More Layoffs Are Coming. Here Are Hard-Earned Lessons From A Former CEO Who’s Been There On Doing RIFs The Right Way

Illustration: Dom Guzman