OpenAI recently announced that it is introducing advertising to its search results. They promise that ads won’t influence results and that no user data will be shared with advertisers. If we take them at their word, what they’ve created is pure noise injection into a product people use precisely because it cuts through the noise.

We’ve seen this movie before. It doesn’t end well.

The Google Playbook

When Google launched in the late 1990s, its clean interface was a revelation. Competitors like AltaVista and Yahoo had become cluttered portals, stuffed with banner ads, pop-ups, and promotional content. Google offered something radical: a simple search box and relevant results. Users flocked to it precisely because it respected their attention.

Then came the advertising. Slowly at first. Text ads, clearly labelled, separated from organic results. Google insisted advertising would never compromise the quality of its results. The company’s founders even wrote in their original academic paper that “advertising-funded search engines will be inherently biased towards the advertisers and away from the needs of consumers.” Google would later adopt the motto “Don’t be evil.”

They were right about the bias. They just didn’t realize they were describing their own future.

Today, Google search results are dominated by paid placements. The first screen of results for many commercial queries contains nothing but advertisements. Organic search engine optimization has become nearly impossible for small players unless they’re willing to pay for placement. Users have learned to scroll past the first several results automatically, knowing they’re looking at whoever paid the most rather than whoever answers their question best.

The degradation happened gradually, which made it easy to rationalize each step. A few more ads here. A slightly less clear distinction between paid and organic there. Each change was small. The cumulative effect transformed a tool built on relevance into a tool optimized for revenue extraction.

In 2018, Google quietly removed “Don’t be evil” from the preface of its code of conduct.

OpenAI is stepping onto the same path. The destination is predictable.

The Economics of Attention Extraction

Think about what advertising actually does. At its core, it’s “rent seeking”. Someone pays to interrupt your attention, inserting their message between you and what you’re trying to accomplish. The advertiser gets access to your eyeballs, the platform collects the toll, and you get... distraction.

This isn’t value creation. Value creation means making something more useful, more efficient, more effective. Advertising does the opposite. It makes the experience worse in exchange for revenue extraction. The only reason platforms can charge for advertising is because they’ve assembled an audience. They’re not creating value. They’re monetizing access.

Some argue that advertising-supported products democratize access, making services available to people who couldn’t otherwise afford them. This framing obscures what’s really happening. Users aren’t getting something for free. They’re paying with attention, with degraded experience, and with the cognitive load of constantly filtering noise. As platforms chase diminishing returns with increasingly aggressive tactics, this cost compounds. The “democratization” argument treats attention as worthless. It isn’t.

The Incentive Problem

When you build a business on advertising revenue, you create a fundamental misalignment between your stated mission and your actual incentives. Your users become the product. Advertisers become the real customers. Every product decision must balance what serves users against what maximizes advertising revenue.

This tension corrupts everything it touches. Social media platforms optimize for engagement through provocation rather than well-being because engaged users see more ads. News organizations chase clicks over quality because page views drive revenue. Search engines must constantly resist the temptation to let advertising influence results because their entire business depends on keeping advertisers happy.

Google’s evolution proves that this resistance eventually fails. No company sets out to degrade its product. But when advertising revenue grows large enough, it becomes the gravitational centre of the business. Everything else bends toward it.

OpenAI claims its advertising won’t influence results. Maybe that’s true today. But incentives shape behaviour over time. When a significant portion of your revenue comes from advertisers, it becomes harder to make decisions that might reduce advertising effectiveness. Google’s founders knew this. They warned against it. Then they did it anyway.

The Arms Race You Can’t Win

Here’s another problem with advertising-based business models: your most valuable users have already opted out, and showing them ads actively drives them away.

I run ad-blockers, DNS filters, and privacy extensions that mute the vast majority of advertising across every platform I use. I’m not unusual. Globally, ad-blocker usage continues to climb, with some estimates suggesting over 40% of internet users now use some form of ad-blocking technology. Among tech-savvy users, the percentage is far higher.

This creates a troubling dynamic. The users who block ads tend to be more technically sophisticated, more educated, and often more affluent. They’re precisely the audience advertisers most want to reach, and precisely the users most likely to pay for a premium product. Yet they’ve built walls specifically designed to keep advertising out.

The damage goes beyond lost impressions. When OpenAI introduces advertising to its free tier, it sends a signal to these users: we see you as a resource to be harvested rather than a customer to be earned. That signal pushes potential paying customers toward competitors who respect their attention. You’re not just failing to monetize them with ads. You’re actively repelling the users most likely to convert to paid subscriptions.

What remains is an increasingly adversarial arms race. Platforms develop more aggressive ad delivery. Users deploy more sophisticated blocking. Platforms try to detect blockers and restrict access. Users find workarounds. Each escalation degrades the experience and builds resentment rather than loyalty.

Building a business on advertising means building on a foundation that your most valuable users are actively undermining. Every year, the tools to block advertising get better, more accessible, and more widely adopted. You’re betting your revenue on a delivery mechanism that an increasing share of your audience refuses to accept.

The Free Tier Fallacy

Many companies justify advertising by arguing that it supports a free tier, which drives user growth, which eventually converts to paying customers. This sounds reasonable. It’s also often wrong.

Free users acquired through an advertising-supported experience are different from users who experience your product at its best. They’ve learned that your product includes distractions. They’ve experienced a degraded version of what you offer. When you ask them to pay, you’re asking them to upgrade from mediocre to good, rather than from good to great.

Compare this to the freemium model done better. Spotify’s free tier includes ads, but it also has limitations that make the paid experience genuinely better: shuffle-only playback, lower audio quality, and no offline listening. The ads aren’t the primary difference between free and paid. The product capabilities are. This gives users a taste of value rather than a dose of friction.

When ad removal is the only meaningful difference between your free and paid tiers, you’ve essentially admitted that advertising is the problem. You’re charging users to escape an experience you deliberately made worse. That’s not a value proposition. That’s a ransom note.

The smart approach is to make the free tier a showcase of your core value proposition, limited by quantity rather than quality. Give users fewer queries, less storage, or restricted features. But make every interaction excellent. Let them experience what paying customers experience, just less of it. This creates a desire for more of something good rather than relief from something annoying.

What Advertising Really Signals

OpenAI built its reputation on providing useful, focused responses. Adding advertising means deliberately degrading that service. Sure, they need a business model that works. But choosing advertising reveals something important about their priorities.

The strategic error runs deeper than lost revenue. When you treat non-paying users as resources to extract value from rather than customers to convert, you optimize for short-term revenue at the cost of long-term brand and loyalty. Every ad impression is a small withdrawal from your trust account. Over time, those withdrawals compound.

There’s nothing wrong with charging for value. Subscription models work because they align incentives. You pay, the company delivers value, everyone wins. Advertising breaks that alignment. The customer becomes the product. The advertiser becomes the customer. The original user gets a noisier, less useful experience.

When a company chooses advertising over direct monetization, pay attention. It tells you who they think their real customer is.

A Better Path Forward

The most sustainable technology businesses are built on straightforward value exchange. You create something useful. People pay for it. You use that revenue to make it more useful. They continue paying. This virtuous cycle aligns everyone’s interests and creates compounding value over time.

Advertising-based models look attractive because they offer faster growth. You can acquire users without asking them to pay. You can scale without friction. But this growth often proves illusory. Users acquired cheaply tend to be less engaged and less loyal. Revenue per user tends to be lower. The constant pressure to serve advertisers pulls the product away from serving users.

Meanwhile, the technical infrastructure to avoid advertising entirely gets better every year. Browser extensions, network-level filtering, privacy-focused alternatives to mainstream services. The advertising model depends on capturing attention, and users are getting better at protecting theirs.

For startups considering their business model, the lesson is clear. If you’ve built something genuinely valuable, find a way to charge for it. Use free tiers strategically to demonstrate value, not to maximize reach through degradation. Resist the temptation of advertising revenue, no matter how appealing it seems in the short term.

Google once promised that advertising would never compromise search quality. OpenAI is making the same promise today. History suggests we know how this ends.

The companies that endure are the ones that never forget who their real customer is. It’s the person using the product, not the person paying to interrupt them.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in a world that refuses to hold still, by thinking strategically and acting tactically.

Find out more at https://coachdavender.substack.com/about
and https://linkedin.com/in/coachdavender.

The founder had been consulting to government agencies for twelve years. Her firm specialized in a technical domain so specific that most people outside the sector couldn’t spell it, let alone understand why it mattered. Government departments called when they needed her expertise. European agencies flew her team across the Atlantic. American organizations paid premium rates for knowledge nobody else possessed.

Now she wanted to build software that captured what her consultants knew, turning her experience into a product and scaling her team’s expertise beyond billable hours.

She believed that to execute her plan, she needed money. The investor across the table had one question: “What’s your TAM?”

She did the math out loud. Maybe 200 organizations worldwide needed what she was building. If half adopted the platform at $50,000 annually, that’s $5 million in recurring revenue. Profitable. Defensible. Built on relationships that took a decade to establish.

The investor’s face said everything. Five million wasn’t a market. It was a rounding error.

But here’s what that investor missed. Sometimes a small, knowable market isn’t a limitation. It’s the entire strategy.

The Billion-Dollar Trap

Venture capital runs on a specific math. Fund ten companies, expect seven to fail, two to return the investment, and one to deliver the outsized return that makes the whole portfolio work. That math requires hunting for billion-dollar outcomes. It’s not greed. It’s geometry.

Which means VCs need founders chasing massive markets. A technology that can capture even 1% of a trillion-dollar opportunity justifies the risk, which is why the pitch deck demands the hockey stick. The revenue model assumes exponential growth. The entire framework optimizes for scale at speed.

These expectations create a predictable distortion. Founders learn to inflate their market projections by broadening definitions until the numbers look venture-scale. That consulting firm? She could have repositioned her platform as “government digital transformation software” and claimed a TAM in the billions. She could have gestured vaguely at “enterprise clients” beyond government. She could have manufactured the market size investors wanted to hear.

Except none of that would have been true. And truth matters when you’re building something designed to last.

Redefining Market Metrics for Focused Ventures

The Holy Trinity of venture market sizing metrics, TAM, SAM, and SOM, absolutely applies to niche markets. However, for specialized ventures, these metrics measure something other than pure growth potential, serving as a tool for proving viability and forcing strategic focus.

Total Addressable Market (TAM) in a funding conversation describes the total possible demand for a product or service, or the revenue opportunity for a company that captures 100% of the market share, with no competition. Of course, TAM is a fantasy because no company can saturate the market, but it is a useful starting point for the discussion. For a niche market, TAM still matters, but the definition shifts. Instead of “everyone who might possibly need this,” TAM becomes “everyone who actually has the problem we solve.” That government platform? The TAM isn’t every government agency on Earth. It’s the specific departments within specific jurisdictions that face the exact regulatory and operational challenges the software addresses. That could be 200 organizations. Maybe it’s 500. The number matters less than the precision.

Serviceable Available Market (SAM), a subset of TAM, describes the targeted market segment that is further narrowed by real constraints. Which of those organizations can you actually reach? Our consulting founder has existing relationships across Canadian provinces, established credibility with federal departments, and proven delivery in select international markets. That’s her SAM. Not a theoretical estimate, but a mapped network of reachable customers.

Serviceable Obtainable Market (SOM) or the slice of SAM that you expect to have captured at maturity, becomes the most interesting metric in a niche business model. In mass markets, capturing 1% looks ambitious. In specialized markets, anything less than 30% suggests you don’t understand your advantage. When you’ve spent twelve years becoming the recognized expert in a domain, when customers already pay premium consulting rates for your knowledge, when the platform productizes expertise nobody else possesses? You should expect to win most of the market you can reach.

The Bottom-Up Reality Check

Traditional TAM-SAM-SOM top-down market analysis fails in specialized domains. Industry reports aggregate broad categories that miss the nuance. “Government software” lumps together everything from payroll systems to specialized regulatory compliance tools. The numbers look enormous but tell you nothing about whether 200 agencies will pay $50,000 for your specific solution.

Bottom-up sizing forces precision. Start with the count. How many organizations actually have this problem? Not “could potentially use this” but “actively struggle with this and currently solve it through expensive consulting.” List them. Name them. Know them.

Then estimate realistic revenue. What do these customers currently pay to solve the problem? Our consulting founder knows this number exactly because she’s been invoicing it for a decade. A typical consulting engagement runs $200,000 annually. A software platform that captures 80% of that value could reasonably charge $50,000 to $75,000. That’s not a guess. It’s anchored in demonstrated willingness to pay.

Multiply actual customers by realistic pricing. If 200 organizations exist and you can reach 150 through existing relationships, that’s your SAM. If you convert 50 over three years based on proven delivery and established trust, that’s your SOM. Fifty customers at $60,000 each is $3 million in annual recurring revenue. Add consulting engagements for implementation and customization, and the business generates $5 million with gross margins above 70%.

Is that venture-scale? No. Is it a viable business that could generate substantial returns for a founder-owner? Absolutely.

When Small Markets Signal Strategic Clarity

The founder who can precisely define a small market understands something essential about momentum. She knows her beachhead isn’t a stepping stone to adjacent markets. The beachhead is the entire defensible territory.

This creates permission to focus. No need to build features for hypothetical customer segments. No pressure to chase expansion opportunities before mastering the core. No temptation to raise capital to fund growth into markets where you lack expertise or relationships. The market is knowable. The customers are reachable. The problem is solvable.

That constraint forces discipline. When you can’t rely on a massive addressable market to forgive execution mistakes, you build execution capability before expanding commitments. You validate that the platform actually delivers the value you promise. You ensure customers succeed measurably. You turn early adopters into references that de-risk decisions for later buyers.

This is Momentum Scaling in its purest form. Each customer success builds capability. Each implementation teaches you something that makes the next one smoother. Each reference strengthens your position. You’re not trying to grow before you’re ready. You’re integrating so deeply into your customers’ operations that switching costs make you nearly irreplaceable.

The Penetration Paradox

Here’s what changes in niche markets. In mass markets, reaching 1% penetration is an achievement. In specialized markets, anything less than 30% penetration suggests you haven’t earned customer trust.

Think about the dynamics. These 200 organizations all know each other. They attend the same conferences. They face the same regulators. They watch what their peers adopt. When three provincial governments successfully deploy your platform, the fourth doesn’t need to be convinced. They need reassurance that you can handle their specific requirements.

High penetration becomes achievable because the market is bounded. You’re not trying to reach millions of anonymous consumers. You’re building relationships with a finite set of sophisticated buyers who make decisions based on evidence and peer validation.

The economics shift too. Customer acquisition costs drop dramatically when every successful deployment generates referrals within a tight professional network. Customer lifetime value is higher when switching costs are high and alternatives are limited. A venture capturing 40% of a $10 million market with 90% retention and minimal acquisition cost can be more profitable than one chasing 2% of a billion-dollar market with high churn and expensive growth.

Building from a Position of Strength

That consulting founder realized that she never needed to raise venture capital. She built the platform using revenue from ongoing consulting engagements, charging early customers for co-development partnerships. Three years later, she has twenty government clients, $2.5 million in software revenue in addition to the continuing consulting revenue, and optionality about what comes next.

She may raise growth capital from patient investors from a position of demonstrated traction. Maybe she stays independent and compounds value over time. Maybe she discovers adjacent markets once the core is unassailable. The point is she gets to choose because she didn’t sacrifice strategic clarity for inflated market projections.

The lesson isn’t that small markets are always superior to large ones. It’s that a precisely defined small market you can dominate beats a vaguely defined large market where you’re guessing. Defining your scaling strategy using the TAM-SAM-SOM methodology works for niche ventures when you use it to prove viability and force focus, not to manufacture venture-scale narratives.

Sometimes the market is exactly as big as it needs to be. Your job isn’t to pretend otherwise. It’s to build something so valuable within that boundary that the boundary becomes your moat.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in a world that refuses to hold still, by thinking strategically and acting tactically.

Find out more at https://coachdavender.substack.com/about
and https://linkedin.com/in/coachdavender.

RenoRun had everything a startup could want. Founded in Montréal in 2016, the company offered an elegant solution to a genuine pain point: an Instacart-style platform for construction materials, promising two-hour delivery of lumber, drywall, and supplies directly to job sites. Contractors who had been losing hours on supply runs finally had a better option. The value proposition was clear, the market was large, and investors took notice.

By 2022, RenoRun had raised over $200 million CAD in venture funding, including a $181 million Series B. The company had expanded from Montréal into Toronto and several major US cities, grown from 130 employees to nearly 600, and built an impressive network of warehouses and delivery fleets. By every venture capital metric, RenoRun was succeeding spectacularly.

Then the ground shifted. Rising interest rates, inflation, and weakening consumer confidence slowed residential construction. Demand softened just as RenoRun’s funding environment tightened. The company cut 12 percent of staff, then another 43 percent. Multiple attempts to raise bridge financing failed. In April 2023, RenoRun abruptly shut down operations, filed for insolvency, and sought creditor protection in Québec.

The fundamental problem wasn’t demand. It was the inability to execute sustainably when conditions changed.

RenoRun’s collapse reveals the gap between building with atoms and building with bits. Venture capital gravitates toward software because it fits a specific model: infinite scalability with near-zero distribution costs. Add users, add servers. Deploy updates with a keystroke. Scale without manufacturing lines, warehouse networks, or delivery fleets.

RenoRun’s business was capital-intensive and labour-intensive. Local warehouses required leases, equipment, and inventory. Vehicle fleets needed maintenance and fuel. Drivers needed wages and benefits. Tight operational coordination across sprawling metros meant complexity that couldn’t be automated away. The model carried heavy exposure to fuel prices, wage inflation, and fluctuations in construction demand. When the macro environment shifted, the cost base couldn’t unwind quickly enough.

This pattern repeats across hardware and physical product ventures. Smart Nora, the Canadian sleep tech company, developed an AI-driven anti-snoring device that achieved over 100,000 unit sales and generated more than $30 million in potential lifetime revenue. Subsequently, the new U.S. tariffs increased Chinese manufacturing costs. Margins evaporated overnight. Their entire business model became unsustainable, and they filed for bankruptcy. Not because their product failed, but because the physics of cross-border manufacturing collided with political reality.

Frank & Oak faced similar constraints when scaling its online fashion retail model. As sales increased, customers began reporting declining quality and inconsistent sizing. These operational failures destroyed the word-of-mouth reputation essential to direct-to-consumer brands. Manufacturing readiness, supply chain reliability, quality control, production capacity, and team capabilities all created potential failure points that software founders never encounter.

The economics tell the story most clearly. Deep-tech startups, hardware companies, and complex service businesses face longer sales cycles, higher customer acquisition costs, elevated operational risks, and higher marginal costs that never disappear with volume. Every unit of a physical product carries real fixed and marginal costs. Your supply chain becomes your ceiling. One quality failure triggers recalls that gut both margins and reputation.

Where software ventures optimize for speed to customer acquisition, hardware ventures must prioritize quality, reliability, resilience, and efficiency from day one. The scaling advantage doesn’t come from capturing market share quickly. It comes from value innovation, from providing greater utility for end users with sustainable unit economics as demand increases.

This creates inevitable tension with investors who want software metrics from hardware companies. They push for hypergrowth trajectories that make sense when deploying code but become destructive when managing manufacturing lines, coordinating global supply chains, and maintaining quality control across thousands of physical units. The pressure compounds because venture capital’s structural constraints favour software economics. Funds need portfolio companies that can achieve exponential growth within their ten-year lifecycle.

Hardware’s longer development cycles, higher capital requirements, and unit-based economics make it harder to generate the outsized returns that justify venture investment. This pushes hardware founders toward strategies designed for software companies, strategies that ignore the fundamental physics of building with atoms instead of bits.

The founders who succeed in hardware recognize this mismatch early. They understand that competitive advantage comes from execution capability: building organizations that consistently deliver quality at scale while maintaining profitable unit economics. They resist the siren song of software-style hypergrowth long enough to build the execution capability that makes scaling possible.

The question isn’t whether hard tech ventures can scale. The question is: which metrics measure progress toward sustainable scale when you’re building with atoms rather than bits? Speed to customer acquisition works brilliantly in software. It creates a disaster when your product must be manufactured, tested, shipped, and potentially recalled.

If you’re building a hard tech venture, what are you measuring beyond user growth and revenue? How are you tracking manufacturing yield rates, quality control pass rates, supply chain resilience, and margin improvement over time? These operational metrics matter more than valuation multiples when physics fights your growth plan.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in a world that refuses to hold still, by thinking strategically and acting tactically.

Find out more at https://coachdavender.substack.com/about
and https://linkedin.com/in/coachdavender.

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Everyone’s debating whether AI will replace knowledge workers. Founders obsess over which roles become obsolete, which tasks get automated, and how to reorganize teams around AI capabilities. Meanwhile, the actual threat unfolds more quietly: AI is making it easier for your team to avoid collaborating with each other.

Why negotiate shared understanding when you can ask ChatGPT? Why struggle through the messy work of aligning across departments when each team can generate their own AI-assisted analysis? Why break down silos when everyone can be productive in isolation?

The Slow Erosion of Collective Capability

Teams operate at three levels of working together. At a basic level, choreography means executing predetermined tasks independently. As maturity evolves, cooperation means pursuing aligned individual goals towards a common outcome. The highest level, collaboration, requires tighter coordination of efforts toward shared outcomes and mutual benefit.

AI makes teams dramatically better at cooperation but worse at collaboration. Each person becomes more productive in isolation. They generate better documents, faster analyses, and more polished presentations. Individual output metrics climb. Leadership celebrates the productivity gains.

Meanwhile, the company’s overall capability to innovate declines.

More dangerously, people stop having the unscripted conversations where breakthroughs happen. Why wander down to another department when you can stay at your desk and let AI bridge the gap? Why invest time in messy cross-functional discussions when you can each work efficiently in parallel?

When a “Failure” Becomes a Billion-Dollar Product

In 1968, Spencer Silver, a chemist at 3M, was trying to develop a super-strong adhesive. He failed. What he created instead was pathetically weak. It barely held paper together and peeled off easily. By any reasonable metric, this was a dead end. Silver should have documented the failure and moved on to more promising projects.

Instead, he kept talking to colleagues across departments about his “failed” adhesive for years. Most people didn’t see the point.

Then, in 1974, Art Fry, a chemical engineer in a completely different division, was singing in his church choir. He used scraps of paper to mark hymns in his book, but they kept falling out. During one of Silver’s cross-departmental presentations about his weak adhesive, Fry made an unexpected connection: what if this “failure” was precisely what he needed for bookmarks that wouldn’t damage pages?

The two collaborated with others at 3M to explore applications neither had imagined on their own. They weren’t optimizing within their silos. They were building on each other’s incomplete thinking, taking imaginative leaps together, willing to look foolish in pursuit of a bookmark made from failed glue.

Post-it Notes launched in 1980. They’ve generated billions in revenue. The innovation required six years of cross-silo conversation, the vulnerability to keep championing a “failure,” and the serendipity of someone from an entirely different context recognizing unexpected value.

Now imagine that story in an AI-mediated workplace. Silver asks AI how to improve his adhesive formula. AI, trained on past patterns and existing solutions, suggests optimizations within the current problem frame: different polymers, varied curing processes, and alternative bonding agents. All focused on making the adhesive stronger because that was the original goal.

AI excels at pattern recognition from existing data. It struggles, though, with the imaginative reframing that humans do naturally: what if weakness isn’t a bug but a feature? What if we’re solving the wrong problem entirely?

More critically, Silver never had the repeated cross-departmental conversations that kept his “failure” alive for six years. Why would he? He got his answer. Fry never hears about the weak adhesive at all. He asks his AI for solutions to his bookmark problem and is directed to existing products or simple DIY approaches.

Neither has the unscripted conversation in which a breakthrough occurs. The company stays “productive” while its capability for serendipitous innovation atrophies. Not because AI prevented them from collaborating, but because AI made collaboration unnecessary for their immediate needs.

Where Innovation Actually Comes From

True innovation doesn’t emerge from optimized individual productivity. It comes from serendipitous discovery. Engineering overhears a customer success call and recognizes a pattern no one else saw. Product and sales connect dots that neither could see alone. Someone takes a leap of faith based on incomplete information and collective intuition.

These moments require genuine collaboration: people communicating across silos, building on each other’s half-formed ideas, making imaginative leaps together. The messy back-and-forth where one person’s confusion sparks another’s insight. The vulnerability of sharing incomplete thinking. And the trust required to collectively explore dead ends.

AI can synthesize existing knowledge with impressive speed. It can identify patterns in data, generate variations on known solutions, and optimize within established parameters. But it can’t create the unexpected connections that happen when humans with different contexts, constraints, and mental models genuinely collaborate.

AI can’t make the intuitive leaps that come from deep mutual understanding. It can’t sense when someone’s hesitation signals an important objection or when their enthusiasm indicates breakthrough potential. It can’t build the shared context where half-articulated ideas are completed by others who truly understand what you’re aiming for.

Innovation requires the willingness to be wrong together, to build on barely formed hunches, to follow curiosity into unexplored territory. This demands the kind of trust and mutual investment that only develops through genuine human collaboration.

The Efficiency Trap

Here’s what makes this shift invisible: AI doesn’t just make staying in your silo rational. It makes it more efficient.

I experience this myself. When I have a question, asking AI gives me an answer immediately. No scheduling conflicts. No navigating different communication styles. No building context. No need to wait for someone to get back to me. The productivity gain is real and immediate.

What I lose is the synergy of human communication. The unexpected tangent that leads somewhere valuable. The trust that is built through repeated interaction. The collaborative relationship that makes future breakthroughs possible.

This asymmetry is the trap. You feel the efficiency gain right away, but the collaboration cost accumulates slowly and invisibly. By the time you notice your team has stopped genuinely collaborating, the relationships and habits that enable collaboration have already atrophied.

AI makes individual productivity measurable and immediate. Collaborative capability remains fuzzy and long-term. Organizations optimize for what they can measure, and the drift toward isolation occurs even when people intellectually understand the value of collaboration.

Why struggle through the difficult work of aligning with other departments when you can be “productive” alone? Why invest energy in understanding someone else’s context when AI can provide good-enough answers? Why sit through uncomfortable discussions about competing priorities when everyone can optimize their own domain?

The cost is invisible until you need genuine innovation and discover your organization has lost the capability to generate it.

What Gets Lost

Real collaboration requires something AI actively discourages: showing up incomplete, vulnerable, and uncertain. AI encourages us to arrive polished and finished, with answers rather than questions, with solutions rather than confusion.

But that messiness is where serendipity lives. Where imagination makes unexpected leaps. Where innovation happens.

You lose the friction that generates insight. You lose the confusion that sparks creativity. You lose the trust that enables risk-taking. You lose the shared context in which half-formed ideas get completed by people who understand what you’re reaching towards.

Most critically, you lose the relationships that make collaboration possible in the first place. When team members default to AI rather than to each other, they stop building the mutual understanding and trust that breakthrough innovation requires.

The Hidden Choice

Your team faces a choice, though most founders don’t realize they’re making it.

You can let AI make everyone more productive in isolation, celebrating efficiency gains while your collective capability atrophies. You can measure individual output, reward personal performance, and watch your scaling constraint tighten. In theory, you could have both AI-enhanced individual productivity and strong collaborative practices. In practice, the immediate efficiency of AI-mediated individual work crowds out the slower, messier, harder work of genuine collaboration unless you’re intentional about protecting it.

Or, you can recognize AI as a tool that makes genuine human collaboration more valuable, not less. When AI handles routine questions and individual production, it should free humans to focus on deeper work. The kind that produces serendipity and breakthrough innovation. But this only happens if you deliberately create space for it.

But first, you need to understand the collaboration constraint that AI is actively making worse. Most founders think they’ve built collaborative teams when they’ve only created collaboration theatre. Brainstorming sessions, collaboration tools, “one team” rhetoric, while people protect territories and optimize individually underneath.

I wrote about collaboration as the the hidden constraint in scaling and how to move from your team culture competition to collaboration. Before you can leverage AI strategically, you need to see clearly what it’s preventing you from building.

The ventures that will win aren’t those that deploy AI most aggressively for individual productivity. They’re the ones that use AI while strengthening their collaborative capacity through human leadership. They recognize that as AI makes isolated work easier, the competitive advantage shifts entirely to organizations that master genuine human collaboration.

The question isn’t whether AI will replace your team. It’s whether you’ll let it replace the human collaboration that drives genuine innovation. That’s the difference between building a venture that creates the future and one that fights for what’s left of the present.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in an unpredictable world by thinking strategically and acting tactically.
Find out more at https://coachdavender.substack.com/about and https://linkedin.com/in/coachdavender .

Listen to this essay:

Last week, I had two conversations with founders struggling with the same problem from opposite directions.

The first founder, who was launching a B2B2C app, wanted a billion-dollar valuation. Fast. When I asked how, he rattled off market size calculations and competitor valuations. What he couldn’t articulate was the transformation his product would create or the capability he’d need to deliver it.

The second founder had developed an AI application in the health technology space that addresses a real problem and demonstrates promising early traction. When I suggested her venture could be worth $10 million in a seed raise, she recoiled in embarrassment. “That seems crazy. I want to be conservative and just build something useful.”

Both founders face the same fundamental problem. Their ambition doesn’t match the opportunity in front of them. One scaled his ambition beyond his capability to execute. The other limited her ambition to less than what her potential warrants. Both mismatches will cost them.

The Cost of Playing Too Small

Here’s what founders miss about limiting ambition: it doesn’t protect you from risk. It just guarantees you’ll underinvest in building the capabilities your opportunity requires.

The founder uncomfortable with $10 million still faces all the uncertainty of building a venture. She still risks time, capital, and reputation. However, she is now doing it without building the systems, team, and infrastructure that could support sustainable growth. She’s taking founder-level risk for diminished returns. That’s not conservative. That’s inefficient risk-taking.

When ambition is too small, you make decisions that compound into mediocrity. You avoid building the systems and capabilities your opportunity requires. You hire for today’s needs when you should be building for tomorrow’s scale. You chase short-term revenue when you should invest in positioning. Each choice feels prudent, but together they guarantee you’ll never build momentum.

Mike McDerment started FreshBooks in 2003 from his parents’ basement after accidentally deleting an important invoice for his web design business. He understood his real opportunity: accounting software simple enough for non-accountants. If that was genuinely possible, the market was enormous.

McDerment gave himself permission to pursue that scale, even from a basement. He moved home to save money. He generated cash with a side gig to fund FreshBooks. These were strategic choices aligned with an ambition that matched his market.

FreshBooks bootstrapped for over a decade. By 2014, when he decided to close his first institutional investment, more than 10 million people had used the platform. Today, it’s valued at over $1 billion. If McDerment had capped his thinking at “a nice lifestyle business”, a plausible assumption given the environment and his experience, he would have underinvested in the systems and the team required to serve millions. Modest ambition would have guaranteed modest results, regardless of the opportunity he was actually addressing.

The Cost of Playing Too Big

The opposite mistake costs just as much, just faster.

When Jean-François Gagné walked into Element AI’s offices in 2017, his high-flying Montréal AI startup was on track to raise $270 million. Co-founder Yoshua Bengio would soon win the Turing Award. Partnerships with Microsoft, Intel, and Nvidia were making headlines. The company opened offices in Toronto, London, Singapore, and Seoul. It launched products in the insurance, manufacturing, and logistics sectors. Element AI had everything: world-class researchers, unlimited capital, and political backing.

Four years later, the US conglomerate ServiceNow acquired Element AI for approximately $230 million, less than half of its peak valuation and significantly less than what the investors had contributed. Most employees got layoff notices. The entity symbolizing Canada’s AI ambitions ceased to exist, absorbed into a larger corporation that wanted its IP but not its business.

Element AI didn’t fail due to a lack of resources. It failed because its ambition far exceeded its capability to execute, while abundant capital removed the constraints that might have forced discipline.

Without scarcity’s discipline, Element AI never answered: What will we not do? They built AI platforms across industries simultaneously, each requiring different expertise, regulatory knowledge, and sales approaches. Product-market fit remained elusive because they chased a dozen markets simultaneously, never focusing long enough to understand any one deeply.

Given the resources to match their ambition, they thought they could pursue everything, but pursuing everything meant mastering nothing.

The Feasibility Gap

Between ambition and results sits a chasm most founders never examine: the gap between what you want to achieve and what you can actually execute.

Your ambition defines the scale of transformation you’re targeting. Your capability determines what you can actually deliver. When ambition exceeds capability by a small margin, that tension drives growth. You stretch, learn, and build new capacity. But when ambition exceeds capability by orders of magnitude, that gap becomes a death spiral.

Element AI wanted to transform multiple industries simultaneously. But they hadn’t proven they could transform even one. They had abundant capital, but capital doesn’t build execution capability. The billion-dollar ambition came first, and the founders were not shy about repeating it. The systematic capability to deliver never caught up. No amount of funding could bridge that gap because the problem wasn’t resources - it was discipline.

The young founder chasing a billion-dollar valuation faces the same trap. The question isn’t whether he can raise enough capital. The question is whether he can develop the execution capability to deliver transformation at that scale. If your current systems can reliably serve 20 customers, and your ambition requires serving 2000, you need to build the processes, team, and operational maturity to deliver quality at 100x scale. That doesn’t happen by raising more money. It happens through systematic learning and capability development.

Most founders skip this entirely, assuming capital and effort will close the gap. They don’t.

Calibrating Honestly

The work isn’t dreaming bigger or thinking smaller. It requires aligning your ambition with both the market opportunity and your trajectory for building capability.

Start with an honest assessment. What transformation are you creating in customers’ world? Not what you hope to create eventually. What you can prove you’re creating now, with evidence from customer behaviour rather than investor enthusiasm.

Then, examine your ability to develop the capability to deliver. What systems, processes, and team strengths have you built? What’s your current capacity to execute? Where are the gaps between what you can deliver today and what your ambition requires?

The gap should create productive tension, not delusional fantasy. If you’re at $500,000 in revenue, and targeting $5 million, and you can articulate the capability you’ll build to bridge that gap with a clear scaling plan, you’re in productive tension. If your revenue is currently at $500,000 and you are forecasting $50 million in five years without explaining how you’ll build 100x capability, you’re in fantasy.

Coming back to the two conversations, the founder who is embarrassed by the $10 million valuation needs to examine whether her discomfort stems from genuine constraints or from permissions she hasn’t yet granted herself. If her solution transforms how customers access health care, if traction suggests significant demand, and if the economics support scaling, then $10 million might be conservative. Limiting ambition doesn’t protect her from risk. It guarantees underinvestment in the capabilities required to realize the impact she seeks to achieve.

The founder targeting a billion dollars needs harder questions. What capability have you built that suggests you can scale to that level? What evidence from customer behaviour validates your assumptions? If answers rely on market size calculations rather than demonstrated capability and validated learning, the ambition isn’t bold. It’s untethered.

He needs to demonstrate that he can deliver transformation at a small scale before pursuing it at a large scale. Element AI’s failure wasn’t inevitable because ambition was big. It was unavoidable because they never proved they could deliver anything before trying to deliver everything.

Ambition should be uncomfortable, but not delusional. Ambitious enough to matter. Grounded enough in capability that the path involves building rather than hoping. Matched to opportunity rather than vanity.

Get that calibration right, and ambition becomes your compass. Get it wrong in either direction, and you’ll take founder-level risk for mediocre returns. Too small, you underinvest in the capabilities your opportunity requires. Too large, you burn resources chasing scale your capability can’t support.

The question isn’t whether to be ambitious. It’s whether your ambition matches the opportunity in front of you and the capability you’re building to capture it.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in an unpredictable world by thinking strategically and acting tactically.
Find out more at https://coachdavender.substack.com/about and https://linkedin.com/in/coachdavender .

Denis’s three-person team launched their “AI junior associate” legal assistant product in March 2024. Six months later, two well-funded competitors announced “AI for Canadian law firms”. One had raised $8M USD and hired a former LexisNexis VP. The other was a U.S. legal AI unicorn expanding north.

Denis’s product did exactly three things: it drafted first-pass documents, summarized case law, and answered client intake questions, all grounded in Canadian precedents and procedure. He had no global ambitions. This was not intended to be a generic legal copilot. He wanted to improve the workflows of overworked associates in Canadian litigation practices with an innovative tool integrated into their document management systems and Word/Outlook stack.

His first three customers were firms where his co-founder had worked as a law clerk, providing both initial revenue and direct workflow insight. Eighteen months later, Denis’s team had 40 paying firms, onboarding at an accelerating pace. Meanwhile, both of his competitors had pivoted away from Canada, unable to gain a foothold in the market.

Denis’s experience leads to the question every applied AI founder faces: how do you build defensible moats on bootstrap funding that outlast better-capitalized copycats?

Why Traditional Moats Don’t Work in Applied AI

Foundation models, AI infrastructure, and APIs are cheap and accessible. If you’re building applied AI, which I define as products layered on top of foundation models and focused on narrow problems or workflows, your advantage isn’t the model itself. Your product is less about inventing new AI and more about combining user experience, data, workflows, and domain knowledge into something uniquely valuable.

Well-funded competitors can outspend you on marketing and engineering. They can copy your features faster than you can build them. However, here’s the capital paradox: more funding often means slower adaptation and bloated teams defending yesterday’s architecture rather than building tomorrow’s advantages.

When investors ask, “What’s your moat?” they’re asking what becomes harder to replicate as you grow. Denis built exactly that: advantages that compound over time rather than erode with each model update.

Three Principles of Defensible Applied AI

Principle 1: Make Your Product Smarter With Every Customer

Design systems that create unique, compounding assets from usage, which new entrants can’t access. This isn’t about collecting data; instead, you are generating intelligence that improves your product in ways competitors can’t match by just throwing money at the problem.

Focus on process, not volume. Denis designed his product so every interaction improved the system: better prompts, more accurate rankings, refined templates. After 18 months, his system writes like each firm’s senior partners. Every legal document generates firm-specific style preferences, citation patterns, and clause libraries, which are intelligence that competitors cannot replicate.

The tenth customer makes the product better for customers one through nine. The hundredth customer creates value enabled by the 99 previous customers. New entrants have to start at zero.

Measure outcomes, not usage. Denis built lightweight analytics to measure real-world outcomes: hours saved per associate, citation accuracy rates, and document revision cycles. These metrics both improve the product and become the sales story. After 18 months, Denis knows with precision how mid-sized litigation firms draft specific motions, where associates spend revision time, and which workflows create bottlenecks.

Encode domain expertise. Denis worked with five senior law clerks to translate guidelines, playbooks, and best practices into prompts and workflows. He encoded Canadian legal research conventions, Law Society compliance, Quebec bilingual requirements, and citation verification. This encoded expertise made the system behave like a niche expert, which is far harder to copy than user interface screens.

This intelligence advantage compounds fastest early but requires continuous reinvestment as foundation models improve. Like most moats in the real world, it demands constant rebuilding, because it is not a permanent fortress.

When Denis’s U.S. competitor tried to expand into Canada, they built a “Canada add-on”, assuming citation formats were similar enough to adapt quickly. Canadian legal practice isn’t American legal practice with a different spelling. Capital couldn’t retrofit expertise they hadn’t built from the beginning.

This is a classic case of sustaining innovation: incrementally improving existing workflows rather than disrupting them. Denis made associates more productive within familiar processes rather than reimagining legal work entirely. This aligns with how law firms adopt technology: cautiously, through proven improvements to established practices.

Principle 2: Make Your Product Hard to Leave

Build switching costs through workflow integration and behavioural habits, not technical lock-in.

Embed into existing systems. Denis embedded his product into systems lawyers use daily: document management, Word, Outlook, and practice management software. Documents were automatically filed. Time entries got logged. Client communications were associated correctly.

This deep integration took six months. Competitors could match Denis’s drafting capabilities in weeks but couldn’t replicate months of integration work without disrupting the firm’s existing workflows. An effective moat isn’t about technical complexity, but operational entanglement. Ripping out Denis’s system means retraining staff, rebuilding automations, and recreating templates, everything a busy firm cannot afford to do.

Design for behavioural lock-in. Generic chat interfaces create no habits. Denis built structured experiences: intake forms, precedent-aware clause suggestions, and jurisdiction-specific checklists. Associates don’t ask the AI to “draft a statement of claim.” Instead, they fill out a form with jurisdiction, cause of action, parties, key facts, and relief sought. The system generates a first draft following the firm’s style.

Over time, associates learn to work Denis’s way. They develop muscle memory. When competitors try to win them over, the barrier isn’t “their AI versus ours”. It’s “learn an entirely new methodology” versus “keep using what you know”, a switch few firms choose to make.

Go vertical, then adjacent. Denis started with Ontario civil litigation, not “all legal work globally.” This vertical focus made the product dramatically better for the target segment than any horizontal tool could be.

Once Denis owned the domain of Ontario civil litigation, expanding to criminal defence meant working with the same firms, using the same document management system integrations, and building on the same trust relationships.

Principle 3: Orchestrate Your Ecosystem

In risk-sensitive domains, being the safe choice beats being the impressive choice. As I’ve written before, relationships can create stronger moats than technology, especially for bootstrap founders who can’t afford platform economics.

Build trust through governance. Legal work carries malpractice risk. Denis built malpractice-aware guardrails from day one. The system never skips citation verification. It maintains audit trails. It requires mandatory human review before client-facing documents go out.

These features slowed Denis initially. Competitors shipped faster because they treated accuracy as a refinement problem. They built impressive demos where AI drafted complete documents in seconds. Denis built a slower system requiring verification and review.

Eighteen months later, Denis’s approach proved correct. Law firms adopt AI to do real work without increasing risk. Denis’s slower, safer system became what firms trusted for court filings.

This trust compounds. Every error-free month builds confidence. Competitors can add governance features retroactively, but can’t replicate a reputation built from day one.

Orchestrate distribution, don’t build platforms. Platform strategies require substantial upfront investment in infrastructure before demonstrating value. Orchestration creates immediate value from each relationship while building toward ecosystem leadership.

Denis couldn’t afford customer acquisition costs that venture-backed competitors could sustain. Instead of competing on paid marketing, he orchestrated partnerships with platforms his customers already used. He partnered with practice management software providers, serving as their “AI drafting layer”. He partnered with legal education providers to offer continuing education credits. He partnered with regional bar associations for member benefits.

These partnerships took months to establish. They required revenue-sharing, co-marketing commitments, integration work, and relationship-building. A competitor with an $8M war chest could outspend Denis on Google Ads. They couldn’t replicate a year of partnership development overnight.

Denis structured partnerships with revenue-sharing that make his layer more profitable for partners than building their own, but he remained aware that successful orchestration sometimes attracts partner competition. The defence is delivering ongoing value faster than partners can replicate internally.

As I explained in my ecosystem strategy essay, orchestrated ecosystems create value through relationship coordination rather than technology integration. You become indispensable not by owning the infrastructure but by making the whole system work better. Money can buy technology infrastructure. It cannot buy the relationships that make orchestration work.

Cultivate your reputation systematically. Denis’s first five customers became his advisory board, as co-designers invested in his success. He published case studies in legal publications and spoke at Law Society events about AI governance. When firms ask around, they hear about Denis from multiple sources: practice management vendors, bar associations, colleagues, publications, and events.

Why Bootstrap Constraints Create Better Moats

Denis’s constraints created his competitive advantages. He couldn’t chase the global legal market, so he went deep on Canadian jurisdiction. He couldn’t hire AI researchers, so he partnered with working law clerks who knew actual workflows. He couldn’t outspend on marketing, so he built partnerships with trusted vendors. He couldn’t make everything, so he focused on three high-value workflows done exceptionally well.

Not all constraints create advantages. Denis’s constraints worked because he channelled them toward compounding moats rather than just survival. Constraints that force short-term thinking or corner-cutting ultimately destroy value. The difference is strategic intent.

Small teams pivot faster. Denis’s three-person team tests new versions of Claude or GPT, adjusts prompts, and deploys updates within days. Better-funded competitors need cross-team coordination, regression testing, and staged rollouts.

Forced proximity to customers reveals moats that feature requests miss. Denis talks to customers weekly, uncovering workflow pain points and opportunities for behavioural lock-in that are invisible in product roadmaps. Limited resources prevent “everything to everyone” dilution, forcing clarity about what creates defensibility versus what just seems reasonable.

The $8M competitor had resources to build faster, but no forcing function to build differently. They applied standard playbook thinking to a market that rewarded non-standard approaches.

The Profit Filter for Moat Building

Before building any moat-deepening feature, apply the Profit Filter, three questions that separate genuinely defensible advantages from innovation theatre:

1. Will this improve core business metrics within realistic ROI timelines? Denis didn’t build features that might pay off in the long run. Every development had to improve citation accuracy, time-to-draft, or revision cycles within six months. He killed a requested AI research assistant feature because it wouldn’t improve core metrics within this timeline, even though it sounded impressive to prospects.

2. Does this deepen our moat or just add functionality? Denis prioritized features that increased switching costs and data quality over “wow” demos. Integration depth beats feature breadth.

3. What’s the value innovation? Are we creating more value for users while reducing our delivery costs? Denis’s outcome instrumentation created value for firms while lowering his costs.

The Moat Lifecycle

All moats erode. The question is whether you can rebuild faster than competitors can copy. Denis’s moats aren’t permanent, but his three-person team rebuilds them faster than competitors, because constraints forced him to design for evolution from the start.

In five years, the legal tech landscape might shift to entirely different workflows. The competitive advantage isn’t having impregnable defences; it’s having moats that compound faster than they erode, and the agility to build new ones when old ones weaken.

Denis avoids the “moat trap”, or the danger of defending yesterday’s advantages rather than building tomorrow’s. His data accumulation could hit diminishing returns. His document management system integrations could become technical debt if legal tech shifts to cloud-native platforms.

The defence isn’t preventing moat erosion; it’s maintaining the capability to rebuild moats quickly. Bootstrap founders who understand this dynamic outperform better-funded competitors who treat moats as permanent fortifications. In chaotic AI markets, the strongest moat is the ability to adapt and rebuild faster than competitors can copy.

Building Your Moat Over the Next 12 Months

Months 1 to 3: Choose Your Battleground. Pick a vertical narrow enough to own. Identify 2 to 3 workflows where you can be dramatically better. Map integration points which incur real switching costs. Find 3 to 5 domain experts willing to co-design weekly. Select foundation model providers and build switching capability to avoid vendor lock-in.

Months 4 to 9: Build Compounding Mechanisms. Design data generation into product usage as a natural byproduct. Implement outcome tracking from day one. Build one deep integration rather than many shallow ones. Encode domain expertise through ongoing collaboration. Design and implement prompt versioning and A/B testing infrastructure to measure improvements.

Months 10 to 12: Orchestrate Your Ecosystem. Add governance features before customers ask. Document your safety approach in language buyers can share. Build distribution partnership channels. Develop case studies and vertical-specific content. Build model-agnostic abstractions to survive foundation model changes.

Ongoing: Test Your Defensibility. Run quarterly diagnostics: Are customers more locked in? Is your product measurably better because of existing usage? Would new competitors face higher barriers?

Apply the Profit Filter to every feature decision. Track which features drive usage stickiness versus demo impressiveness. Measure time-to-value and switching costs explicitly.

Defensibility Through Design

Denis didn’t prevent copycats from appearing. He built something they couldn’t copy, even when they tried. The well-funded competitor could replicate his interface in weeks, but couldn’t replicate 18 months of accumulated firm-specific intelligence. They could hire impressive AI researchers, but couldn’t encode Canadian legal expertise they didn’t have. They could outspend on marketing, but couldn’t build trust with bar associations and practice management vendors.

For bootstrap founders building in applied AI, this reframe is liberating. You don’t need to out-innovate better-funded competitors on the foundation model layer. You don’t need to out-spend them on customer acquisition. You don’t need to match their team size or marketing budgets.

You need to design systems that turn your constraints into advantages. Limited capital forces vertical focus, creating genuine expertise. Small team size enables customer intimacy, revealing deeper moats. The inability to chase every opportunity creates discipline to build depth over breadth.

The pattern across successful bootstrap ventures is consistent: they understand moat dynamics, leverage orchestration strategies, and filter innovation through profitability. They outperform competitors who mindlessly apply venture-backed playbooks because limited capital forces strategic clarity that abundant resources often obscure.

Think like a system designer, not just a model integrator. Build something that gets smarter with every customer, harder to leave with every integration, and safer to trust with every successful deployment. That’s a moat, not because it prevents competition, but because it creates a compounding advantage that makes the competition increasingly irrelevant.

The question isn’t whether well-funded copycats will appear. In any market worth serving, they will. The question is whether you’ve built something that compounds faster than they can copy.

Denis and the company details in this essay are based on a real Canadian legal AI startup. Names and certain specifics have been changed to maintain confidentiality while preserving the strategic lessons.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in an unpredictable world by thinking strategically and acting tactically.
Find out more at https://coachdavender.substack.com/about and https://linkedin.com/in/coachdavender .

Jacques DeLarochellière and David Brillon sat across the table from a private equity firm in 2020, twenty-eight years after they had started ISAAC Instruments in a Montréal garage. What began as a motorsports telemetry company had evolved through several pivots: from racing to vehicle testing for Ford, GM, and Toyota, then into commercial trucking telematics. By 2020, ISAAC had become a leading driver-centric fleet management platform with a strong Canadian market position and growing U.S. presence. The company was profitable, employed nearly two hundred people, and generated tens of millions in annual revenue.

They faced a choice. The firm wanted to invest, offering growth capital to fuel product innovation and geographic expansion. But here’s what made the moment remarkable: after twenty-eight years of bootstrapped growth, DeLarochellière and Brillon weren’t negotiating from desperation. They were dealing from strength. They could say yes or no. They could set terms. They could retain significant ownership and operational control.

The freedom to make that choice, nearly three decades into their entrepreneurial journey, represents a different form of success than Silicon Valley celebrates. They had built exit optionality.

The Binary Myth

Silicon Valley has conditioned us to measure startup success through a narrow lens: valuation milestones, funding announcements, and eventual exits through acquisition or IPO. By these standards, success is binary. You either build a unicorn or you’re a failure.

This unicorn-or-failure narrative serves investors who need outsized returns on a small number of investments to compensate for their many failures. But it serves most founders poorly because it judges their companies by criteria that have nothing to do with whether they’ve built something valuable, sustainable, and personally rewarding.

The assumption buried in every pitch deck template and accelerator program is that exit is the goal. Build fast, scale faster, and cash out. The timeline is implicit: five to seven years from founding to liquidity event. The path is prescribed: raise seed, Series A, Series B, then either sell or go public.

The venture capital model makes specific assumptions about how value gets created. It assumes markets are large enough to support billion-dollar outcomes, that growth can be accelerated through capital injection, and that the optimal path is to grow as fast as possible and figure out profitability later. These assumptions work brilliantly when they align with reality. Software platforms with network effects, marketplace businesses that benefit from liquidity, and infrastructure plays that require capturing market share quickly all fit this model well.

Some businesses genuinely require venture capital to succeed. When Uber launched, the ride-sharing market required building supply and demand simultaneously across multiple cities. Network effects meant that the first company to achieve critical mass would be extremely difficult to displace. Under these conditions, venture capital wasn’t optional. Similarly, deep technology ventures developing new semiconductor architectures or breakthrough pharmaceutical compounds require years of research before any revenue emerges.

But the unicorn-or-failure narrative obscures an important reality. Most markets aren’t winner-takes-all. Most competitive advantages come from execution excellence rather than first-mover dominance. Most businesses can build profitably if founders resist the siren call of premature scaling funded by abundant venture capital.

The myth isn’t that venture capital exists or that some companies need it. The myth is that unicorn-or-failure represents the only measure of entrepreneurial success. The question isn’t whether the venture path is legitimate. It’s whether it’s the only definition of success worth pursuing.

Two Very Different Paths

The venture path is optimized for spectacular wealth generation through a single liquidity event at an uncertain future date. The momentum path is optimized for consistent wealth generation from day one.

This isn’t just a philosophical difference. The mathematics reveal fundamentally different outcomes. Consider a founder who raises venture capital and aims to exit within 5 to 7 years. If successful, they might own 10% to 15% of a $100 million company, generating $10 to $15 million in pre-tax income. This outcome requires enormous stress and completely consuming focus for years. It also requires accepting someone else’s timeline, their definition of success, and their risk tolerance.

Alternatively, that same founder could build profitably. In five years, they might own 100% of a business generating $5 million in annual revenues with healthy margins. This isn’t just income requiring continued work. It’s a perpetual wealth-generating asset that the founder controls completely. They can reinvest profits to grow. They can take dividends. They can sell when conditions are optimal. The business becomes valuable not just as a one-time exit but as an ongoing source of wealth that compounds year after year.

Month one, $80,000 in revenue funds modest improvements. In month twelve, $150,000 in revenue funds more significant capability-building. Month twenty-four, $300,000 in revenue funds expansion that would have required outside capital earlier. The capital efficiency compounds. The learning compounds. The freedom compounds.

The difference extends beyond mathematics. It’s about what kind of decisions you can make. When you’re not dependent on outside capital, you can say no to customers who don’t fit your ideal profile. You can invest in initiatives with extended payback periods. You can prioritize quality over speed when circumstances warrant. You can weather market downturns without panic.

You can choose whether and when to exit, rather than being forced to pursue liquidity on someone else’s timeline.

Two Forms of Exit Optionality

ISAAC’s choice in 2020 illustrates what exit optionality looks like in reality. After carefully evaluating the offer, DeLarochellière and Brillon chose to partner with the private equity firm. The deal was structured to fuel product innovation and geographic expansion while allowing the founders to retain significant ownership and operational control. DeLarochellière remained CEO, Brillon stayed as CTO, and the company’s culture remained intact.

But here’s the critical point: they made this choice after building a profitable, sustainable business without venture capital. They chose growth capital on their terms, at a moment when it served their strategic objectives rather than their survival needs. They could have said no. They could have continued building independently. They had options.

This isn’t a lifestyle business avoiding growth. It’s a strategic business that built the freedom to choose its own path. The twenty-eight years of patient building created leverage that no amount of early-stage funding could have provided.

Exit optionality takes different forms. Consider Sarah Legendre Bilodeau, Virginie Boivin, and Laurent Barcelo, who built Videns Analytics from a boutique applied AI consultancy in 2018 into a team of thirty specialists operating across Canada, France, and the United States. They positioned Videns around human-centric AI, helping organizations evolve through data in ways aligned with their values. Over seven years, they built hard-won expertise, earned B Corp certification, and established a strategic partnership with Mila, the Québec AI Institute.

By 2025, Videns was generating substantial revenue with healthy margins, entirely bootstrapped. The founders owned the company outright. Then Cofomo, a Canadian IT consulting and digital transformation leader, approached them with an acquisition offer.

The offer was substantial. Not life-changing wealth by venture capital standards, but enough that the founders could walk away financially secure. More importantly, they had laid the foundation to evaluate the offer from a position of strength rather than desperation.

They did the analysis. Each founder answered separately: what amount of money would meaningfully change your life? When they totalled their individual thresholds and worked backwards through ownership structure, transaction costs, and Canadian tax treatment, the Cofomo offer comfortably exceeded their range.

But meeting the threshold wasn’t the same as making the decision. The money worked, but the founders wrestled with what mattered more. They wanted to prove that you could do AI differently, that human-centric and profitable were not contradictions. Would their vision continue inside a larger organization? How about the team and the culture they built over the years?

They evaluated Cofomo carefully. They met with the integration team. They spoke with leaders of other acquired companies. They negotiated on transition terms, team retention, the Mila partnership, and continuation of B Corp practices.

The founders realized something during this process. They had proved that their model works. They had shown that responsible AI and commercial success were compatible. But as a thirty-person consultancy, there was a ceiling to their impact. Inside Cofomo, their methods could reach clients they would never access on their own. The Mila partnership gave them credibility, but Cofomo could provide them with scale. If the acquiring company invested in what they had built, the impact could be far larger than anything they could achieve independently.

In September 2025, Cofomo announced the acquisition. The deal positioned Videns’ thirty experts as a specialized unit within Cofomo’s broader offering. The B Corp certification remained intact. The Mila partnership continued. The founders completed their transition periods.

This represents the other form of exit optionality. Not growth capital while retaining control, but strategic exit on favourable terms. The key is that both ISAAC and Videns chose their paths from positions of strength. Both had built profitable businesses that gave them genuine options. Both evaluated offers carefully against their actual goals rather than accepting whatever was available out of desperation.

Exit optionality means having choices when others face requirements.

The Freedom of Compounding

What makes exit optionality powerful isn’t any single advantage but how multiple advantages compound over time. While venture-funded competitors optimize for one dimension of growth, profitably built companies develop across several dimensions simultaneously.

The psychological advantages compound. When you’re not dependent on outside capital, you make different decisions at every level. Strategic decisions become clearer when you optimize for actual customer value rather than metrics that impress investors. Tactical choices align with long-term goals rather than short-term funding pressures. You can pursue initiatives that might not show immediate returns but build lasting competitive advantages.

Perhaps most powerfully, your personal learning compounds. You’re forced to understand every aspect of your business model because you can’t simply spend money to solve problems. This deep understanding enables better strategic decisions over time. You recognize patterns earlier. You spot opportunities faster. Your judgment improves with each cycle. Your team develops execution capabilities that accumulate over time because you’re building for sustainability rather than explosive growth that can lead to collapse.

The compounding advantage becomes most apparent during market disruptions. When the economy contracts, venture-funded companies scramble to extend their runway and often cut deeply to survive. Profitably scaled companies adjust and continue operating because profitability provides a cushion. This resilience creates opportunity. When competitors struggle, you can maintain service quality and capture displaced customers. When unexpected opportunities emerge, you can pursue them with your own capital rather than spending months pitching investors.

Exit optionality means negotiating from a position of strength rather than desperation. It means building something that gives you freedom, impact, and control over your destiny. It means the choice to exit becomes truly a choice, not a requirement imposed by your capital structure or investor timelines.

The Path Forward

Neither the venture path nor the momentum path is inherently superior. They optimize for different outcomes. The critical question isn’t which path is better in the abstract. It’s which path aligns better with your specific circumstances, capabilities, risk tolerance, and definition of success.

But if you’re reading this, you likely sense that building for optionality better serves your goals. You may be building in a market that’s substantial but not winner-takes-all. You could value control over valuation multiples. Maybe you want to build something sustainable rather than spectacular. Perhaps you recognize that your competitive advantage comes from execution excellence rather than first-mover dominance or scale advantages.

The venture capital playbook promises exponential growth but often delivers fragility and founder burnout. Building for exit optionality delivers what every entrepreneur wants: a successful business that gives you freedom, impact, and control over your destiny, without requiring you to sacrifice everything else that makes life meaningful.

Because in the end, the most important return is not what your venture gives to investors or customers, but what it gives back to you. And the freedom to choose when and whether to exit, on terms you set, represents success that no predetermined exit strategy can match.

This Substack is drawn from my upcoming book, Momentum Scaling: How to Grow a Tech Venture in an Unpredictable World, expected by mid-2026.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in an unpredictable world by thinking strategically and acting tactically.
Find out more at https://coachdavender.substack.com and https://linkedin.com/in/coachdavender .

In April 2020, Quibi launched with nearly $2 billion in funding, Hollywood heavyweight Jeffrey Katzenberg at the helm, former HP CEO Meg Whitman as president, and Super Bowl ads announcing their arrival. Six months later, they shut down.

The conventional explanation blames bad timing: launching a mobile-first service during COVID-19, when everyone was locked at home. But Quibi collapsed because abundance gave them permission to skip the validation that actually matters. When you have nearly $2 billion, you can hire armies of developers, produce Hollywood-grade content, and buy Super Bowl ads without ever proving customers want what you’re building. Quibi treated capital as evidence of product-market fit and never bothered to establish problem-solution fit first.

That’s a billion-dollar mistake hiding in plain sight.

The Validation That Actually Matters

Problem-solution fit requires testing two fundamental hypotheses with real customers before you’re ready to scale:

• The Value Creation Hypothesis answers how your solution delivers meaningful value to customers. This is what Clayton Christensen calls “jobs-to-be-done” fit: understanding how your product integrates into customer workflows and creates value from their perspective, not yours.

• The Value Capture Hypothesis determines what motivates customers to invest in your solution. Why do they choose to pay? What makes your offering worth their money, time, and attention?

Quibi never properly tested either hypothesis.

They assumed people wanted premium, short-form content in bite-sized episodes designed for mobile viewing during commutes and quick breaks. That was their Value Creation Hypothesis. They thought people would pay $5-8 per month for this content, even though YouTube and TikTok offered similar mobile-optimized content for free. That was their Value Capture Hypothesis.

Both assumptions went untested until launch day. Instead, they spent hundreds of millions on content production, proprietary “Turnstyle” technology, A-list talent, and massive marketing campaigns, all of this before demonstrating whether anyone actually wanted what they were building.

They had the resources to conduct proper validation testing. They chose not to.

What Abundance Removes

At the problem-solution fit stage, your job isn’t to build scalable systems or optimize customer acquisition costs. It’s learning. You’re investing time to discover what value customers actually see in your offer and why they choose to buy.

This requires real conversations with real people. Testing assumptions with minimal investment. Building just enough to learn whether your hypotheses hold up when they meet reality.

Quibi could have tested its Value Creation Hypothesis by licensing existing short-form content and seeing whether people consumed it as they predicted. Small pilots with a few hundred users would have revealed viewing behaviour, retention patterns, and whether their content format actually worked during commutes.

They could have tested their Value Capture Hypothesis by offering different pricing models to different user segments, understanding price sensitivity, and discovering whether premium content justified premium pricing in a market saturated with free alternatives.

Spotify offers the counterpoint. Before scaling globally, they ran a closed beta of their web app with a few thousand Swedish users, deliberately recruiting influential music bloggers as early adopters. They wanted to prove their core promise: whether users truly valued “click and music plays instantly”. This was their Value Creation Hypothesis. They focused on behavioural engagement — session length, repeat visits, and people switching from piracy to streaming — rather than vanity metrics like sign-up numbers. They tested their Value Capture Hypothesis, their freemium model with actual ads, before committing to that revenue structure. They expanded into new markets only after demonstrating demand in each.

The difference wasn’t resources. Spotify would eventually raise substantial capital. The difference was sequence: validate first, then scale what’s proven. Quibi reversed this. They raised capital first, then tried to validate at scale with millions of users simultaneously. That’s not testing. That’s gambling.

When you have abundant capital, the forcing function that makes most founders test before building evaporates. Why pilot with hundreds when you can launch to millions? Why iterate cheaply when you can execute expensively?

Abundance doesn’t just enable bad decisions. It removes the constraints that prevent them.

When Assumptions Meet Reality

The numbers delivered the verdict. Quibi peaked at 379,000 daily downloads on launch day, then dropped below 20,000 per day within two months. When Meg Whitman acknowledged reality in October 2020, she admitted “the product market fit was wrong.” But they never established problem-solution fit in the first place; they built and scaled a solution to a problem they assumed existed.

Spotify demonstrated streaming demand during the peak of Napster-era piracy by testing with Swedish users and watching their behaviour. They proved their hypotheses before scaling. If Quibi had done the same by running pilots with actual commuters, testing consumption patterns, and demonstrating willingness to pay, then they would have discovered the weakness in their Value Capture Hypothesis regardless of COVID.

Would busy professionals pay for premium short-form content when free alternatives existed? Would they choose Quibi over YouTube, TikTok, or social media during downtime? Would the content format they envisioned integrate into daily routines?

These questions didn’t require a pandemic to answer. They required customers. Quibi had investors instead, and believed they knew what was best.

The Discipline That Abundance Obscures

Resource constraints force discipline. When you can’t afford to build everything, then first you pilot cheaply. When you can’t hire armies of developers, you test assumptions before writing code. When you can’t buy Super Bowl ads, you prove value with early customers who actually pay.

Abundance removes these forcing functions. Your cash enables you to scale operations before proving assumptions. You can hire before systematizing. You can market before validating. The discipline that constraints impose becomes optional when capital is abundant.

Problem-solution fit demands systematic validation regardless of your bank balance. You must articulate your value proposition in ways that consistently resonate. You must understand the core motivations that drive purchase decisions. You must identify patterns in who buys and why. You must make enough transactions to prove customers will actually pay.

Only then are you ready to build toward product-market fit, which requires repeatable systems, documented processes, and the ability to generate revenue without the founder having to close every deal.

Quibi had Hollywood pedigree, Silicon Valley expertise, and nearly $2 billion in capital. What they didn’t have was evidence that their solution created value people would be willing to pay for. They confused investor belief with customer validation.

The Real Evidence

Capital doesn’t validate hypotheses. Customers do.

Investors believed in Katzenberg’s vision and Whitman’s execution capability. That belief generated nearly $2 billion in funding. But customer belief generates something different: usage, retention, and willingness to pay. Those behaviours can’t be assumed. They must be proven.

Until you’ve tested your Value Creation Hypothesis and your Value Capture Hypothesis with real users in real contexts, you haven’t proven problem-solution fit. And without problem-solution fit, claiming product-market fit is just expensive wishful thinking.

The discipline isn’t complicated:

• Test assumptions cheaply before scaling expensively

• Let customers validate your hypotheses before investors fund your expansion

• Build evidence of problem-solution fit before constructing systems for product-market fit

• Watch what people do, not what they say or what vanity metrics suggest

Abundance makes this discipline optional. That’s precisely when it becomes most essential.

Your Value Creation Hypothesis and Value Capture Hypothesis need evidence, not assumptions. How do you create value for the customer from their perspective? What is their motivation to commit money, time, and attention? These questions demand answers before you scale, and those answers come from watching real customers in real contexts. Capital doesn’t answer them. Only customers can.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in an unpredictable world by thinking strategically and acting tactically.
Find out more at https://coachdavender.substack.com and https://linkedin.com/in/coachdavender .

Jean-François thought $200,000 in revenue meant he was ready to scale. His virtual reality device for medical procedures worked. His initial customers loved it. He had revenue coming in. So, he thought, it was time to hire a salesperson and grow. However, it didn’t work.

This is where most founders get stuck between validation and scaling. They misread their revenue numbers. Although they’ve demonstrated that customers will pay, they haven’t built the systematic capability that makes revenue repeatable rather than sporadic. They’re generating revenue, but they’re not yet in the game.

In the Momentum Scaling model, $1 million in trailing twelve-month (TTM) revenues marks the threshold where you’ve proven you’re actually performing, not just practicing. But not all $1 million is created equal. Here’s why that number matters, what kind of revenue it needs to represent, and why it has to be TTM revenue, not the Annual Recurring Revenue (ARR) metric so many founders misuse.

From Validation to Repeatability

At an initial revenue volume of $200,000 to $500,000, you’re demonstrating problem-solution fit, or that customers see enough value in what you are offering that they will pay something for it. That’s Go-to-Market Maturity Level 1, founder-led validation. Every deal happens differently. Only you can close the sale. Revenue comes in unpredictable bursts whenever you find time to sell between everything else you’re doing. Customers have different motivations, making the ready-to-buy signal even harder to interpret.

This revenue level is necessary to show you have potential, but not sufficient.

By the time you reach $1 million in revenue, you are crossing into Maturity Level 2: building repeatability. You’ve documented what works. You’ve identified patterns in who buys and why. Someone other than you can execute your go-to-market process and generate revenue. Your conversion rates show measurable patterns you can analyze and improve. Your revenue becomes systematic rather than sporadic.

Level 3, predictable revenue, comes later with significantly more customer volume and the statistical reliability that enables accurate forecasting.

The point is this: if your situation is like Jean-François's, with revenue of $200,000, you are still figuring out your value proposition through trial and error. Every sale teaches you something new. At this level, you do not yet have enough data. However, when you’ve reached $1 million, you’ve made enough transactions across enough customer types to have documented a playbook grounded in validated learning rather than founder intuition. You’ve proven repeatability, which is what gets you into the game.

Not All $1 Million Is Equal

You can hit $1 million TTM through three large enterprise deals sold entirely through founder relationships and heavy customization. Or, you can hit this number with 25 mid-market customers who share similar use cases, with strong retention, and a clearly defined ideal customer profile. Both cases are $1 million. But only one signal tells you you’re actually in the game.

The first scenario is founder heroics at scale. You’ve proven you can land big deals through personal networks and custom solutions. That’s valuable, but it’s not repeatable. The second scenario is systematic capability. You’ve identified a specific customer profile, solved a common problem for them in a standardized way, and demonstrated they’ll stick around because the value is real.

Getting to $1 million also forces you to move beyond enthusiastic early adopters into the early majority. These customers have different buying criteria. They want established processes, documented results, and proven track records. They won’t risk their careers on an unproven vendor. This transition from early adopters to early majority is what Geoffrey Moore called “crossing the chasm”.

Ask yourself these questions to assess whether you’re actually in the game at $1 million TTM:

• Depending on your business and pricing model, do you have at least 8 to 12 enterprise customers, or 20+ mid-market and SMB customers, or 1,000 individual SaaS customers, with similar patterns? If your customer base is a grab bag of very different companies using your product for entirely different reasons, you haven’t yet found repeatable product-market fit.

• Is your ideal customer profile clearly defined? Can you describe in specific terms who buys, why they buy, what problem they’re solving, and what success looks like for them?

• Are customers renewing and expanding? Low churn and strong net dollar retention signal that you’re solving a real problem sustainably.

• Can someone other than you close deals using your documented process? If you are the only person who can sell your product effectively, you haven’t built a systematic capability.

$1 million TTM is best viewed as a checkpoint, not a finish line. It’s a signal that you’re moving from idea to real business and may be approaching product-market fit. But you need to dig into the quality of that revenue.

Why TTM, Not ARR

Here’s where founders deceive themselves most dangerously: they confuse trailing twelve-month revenue (TTM) with annual recurring revenue (ARR).

ARR is a projection based on the assumption that customers will continue to pay. For true subscription businesses with contractual commitments and demonstrated retention rates, it’s a useful planning metric. But most founders using ARR don’t have contractual recurring revenue, at least not yet. They have one-time projects, annual contracts without renewal history, or pilot programs they’re calling “ARR” because it sounds better.

The business model distinction matters enormously. A founder with 3 enterprise customers at $333,000 each, claiming “$1 million ARR”, has no statistical foundation for that “recurring” claim. Three data points tell you nothing about retention patterns or renewal rates. When one customer doesn’t renew, you’ve lost 33% of your “ARR” and discovered you never had recurring revenue at all.

Compare that to a founder with 100 SMB customers at $10,000 each who’ve been around for 12+ months. If 95 have renewed, you have actual retention data. You can calculate churn rates, expansion revenue, and net dollar retention. You might legitimately claim ARR because you have statistical evidence that the revenue recurs.

TTM revenue is actual money that actually came in from actual customers who actually paid. It’s truth, not projection. It’s validated execution, not hopeful forecasting.

The self-deception compounds when you use ARR to justify scaling decisions. If you claim “$1 million ARR” that’s really three unrepeated deals as a reason to hire, you’re in trouble. If you raise capital at a valuation predicated on $1 million recurring revenue that you haven’t proven to recur, you will fail. Misjudging the validity of your ARR hits hard when customers don’t renew, and the unit economics fall apart at scale.

TTM forces honesty. It makes you account for actual transaction volume, actual customer diversity, and actual renewal patterns. It makes you prove you can execute repeatedly, not just once.

From Practicing to Performing

Jean-François discovered this the hard way. His first sales hire failed because he tried to delegate founder heroics, thinking that the hard work was done. After turning around and spending three months documenting his process, identifying the patterns that drove successful sales, and building repeatable frameworks, his second hire closed deals independently within six months. The difference wasn’t the person. It was the system. He upped his Go-To-Market Maturity Level.

When you reach $1 million TTM and have a strong, well-balanced customer list, you’ve demonstrated this systematic capability. You have sales materials that clearly communicate value. You have qualifying criteria that identify promising prospects. You have conversation frameworks that move leads toward decisions. Then, training a new member of your business development team takes days rather than months because the system exists independently of you. Now you can scale.

The credibility you’ve established in the market compounds this capability. You have reference customers who vouch for your value. You have case studies that speak to different buyer personas. You have enough implementation experience to anticipate objections before they surface. This becomes the foundation for Horizon 2’s expansion into adjacent markets.

How To Get In The Game

Revenue below $1 million proves you have something worth building. Revenue at or above $1 million with the right customer base mix proves you’ve built the capability to scale.

When I assess a growing tech venture, the $1M TTM threshold represents:

• Enough transactions to validate repeatable go-to-market processes

• Sufficient customer volume with similar patterns, proving this isn’t just founder heroics

• Demonstrated retention and renewal, not just one-time transactions

• Systematic capability that others can execute, not knowledge trapped in your head

Below this threshold, you’re still validating. You’re learning what works through trial and error. That’s necessary work, but it’s practice, not performance. You are not ready to scale.

At $1 million in trailing twelve-month revenues, not ARR projections, not one-time windfalls, not founder-dependent heroics, but actual money collected from actual customers following actual patterns over twelve actual months, you’ve proven you can perform.

Jean-François learned this distinction between a revenue number and execution capability. He had generated $200,000 in revenue but lacked a systematic process to scale it. When he built that capability, documented what worked, and created transferable systems, the revenue followed because he’d moved from practicing to performing.

The question isn’t whether you can get customers to pay once. The question is whether you’ve built the systematic capability to acquire customers repeatedly, serve them profitably, and grow sustainably. Below $1 million TTM, you’re still practicing. At $1 million TTM, with the right composition of your customer base, you’re actually performing because you are proving that you can acquire customers repeatedly through documented systems, not just founder heroics.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in an unpredictable world by thinking strategically and acting tactically.
Find out more at https://www.davender.com and https://linkedin.com/in/coachdavender .

Every founder faces this moment of truth. You’ve built something people want. Now you need to decide what to charge for it. And if you’re like most founders, you default to subscription pricing because that’s what everyone does. Monthly recurring revenue looks good on a pitch deck. Investors love the predictability. Your competitors all do it.

But here’s what gets lost in that reflexive decision: your pricing model isn’t just about collecting money. It’s a declaration about how you create value and where you fit in your customer’s world.

Drowning in Subscriptions

We’re drowning in subscriptions. Your phone bill, your software stack, your streaming services, your project management tools, your analytics platform, your email service, your cloud storage. They add up quietly, month after month, until one day you’re sitting at your desk wondering why your operating budget feels like a slow leak you can never plug.

This isn’t just about cost. It’s about the relationship those recurring charges create. Every month, your customers – whether consciously or unconsciously – vote whether to keep doing business with you, re-evaluating whether you’re still worth it. Every month, you’re competing not just with alternatives but with the simple desire to stop the bleeding.

And yet founders keep choosing this model, not because it best serves their customers, but because it best serves their financial statements. Subscription revenue is predictable. It’s scalable. It makes the growth curve look smooth. But smooth curves don’t always reflect healthy businesses.

What Your Pricing Model Actually Tells Customers

When you choose how to charge, you’re making a claim about value. Subscription pricing says: “We deliver continuous value that integrates into your daily operations.” Project pricing says: “We help you accomplish discrete transformations.” Usage-based pricing says: “You pay for what you consume, and our interests align with your success.” One-time purchases say: “We’re building you a capability you’ll own.”

Each model carries assumptions about how customers budget, how they measure success, and how they think about your place in their value chain. Get it wrong, and you’re not just leaving money on the table. You’re creating friction at the exact moment you should be building momentum.

Consider the distinction between capital expenditures and operating expenses. Operating expenses are about optimizing operations, the costs of keeping things running day to day. These come from budgets customers scrutinize quarterly, optimize relentlessly, and cut when times get tight. Capital expenditures are about building capabilities, investments in tools and infrastructure that create lasting value and expand what’s possible.

If your product helps customers build something that compounds over time, pricing it as an operating expense puts you in the wrong conversation. You end up defending your monthly fee against other operational costs when you should be positioned as an investment in capability.

When One-Time Pricing Makes Perfect Sense

Take Scrivener, the writing software that’s become the standard tool for book authors (like me). While competitors like Microsoft Word and Google Docs shifted to subscription models, Scrivener held firm with a one-time purchase. Buy it once, own it forever.

This wasn’t stubbornness or nostalgia. It was strategic clarity about value creation.

When you’re writing a book, you’re not optimizing a continuous operation. You’re building a capability and executing a transformation. The software becomes part of your creative infrastructure. You learn it deeply, customize it to your process, and build your manuscript inside it over months or years. That’s a capital investment in your writing capability, not an operating expense you rent month to month.

Scrivener’s pricing model aligns perfectly with how their customers think about value. Authors budget for tools that help them complete projects, not for monthly subscriptions that add up while their manuscript sits half-finished. The one-time purchase removes the psychological burden of ongoing costs during the inevitable slow periods when life gets in the way of writing.

Even their trial period reflects this understanding. Thirty days of actual use, not thirty calendar days. If you open Scrivener only twice a week, your trial lasts for months. They’re not trying to pressure you into a quick decision. They’re giving you time to integrate the tool into your creative process, because they know that once you’ve built your workflow around it, once you’ve structured your novel or dissertation or screenplay in their system, you’re not leaving.

This is what pricing aligned with value creation looks like. Scrivener isn’t fighting against how their customers budget and buy. They’re making it easier to say yes by removing the subscription fatigue, the monthly re-evaluation, and the nagging sense that you’re paying for something you’re not using enough.

Beyond Seats and Subscriptions

The pricing landscape is more varied than the subscription orthodoxy suggests. Look past the per-seat model that dominates corporate SaaS, and you’ll find companies experimenting with structures that better reflect how they create value.

AWS doesn’t charge you for seats. They charge for what you consume: compute hours, storage gigabytes, API requests. Your bill scales with your usage, not your headcount. Stripe takes a percentage of every transaction they process, essentially becoming a partner in your revenue rather than a fixed cost centre. Twilio charges per message sent, per call made, and per interaction facilitated.

These aren’t just different ways to collect money. They’re different relationships with value creation. When you pay AWS based on consumption, their success depends on making their services efficient enough that you want to use more of them. When Stripe takes a percentage of transactions, they win when you win. The incentives align.

Then there are companies pushing further into outcome-based pricing. CanIHelp.ai is a text-based support platform which charges per resolved customer issue, not per agent seat or per conversation. You pay only when the problem gets solved. That’s pricing anchored in the outcome customers care about, not the activity that generates it.

The pattern that emerges isn’t about finding the one true pricing model. It’s about matching your pricing structure to where you create value in the customer’s chain. Infrastructure plays? Usage-based pricing makes sense because the value scales with consumption. Transformation plays? Project or outcome-based pricing aligns better because you’re delivering discrete change, not continuous optimization. Capability building? One-time purchases or licensing remove the cognitive load of recurring costs.

What matters is whether your pricing model makes the value obvious or obscures it. Whether it creates alignment or friction. Whether it fits how your customers budget, buy, and measure success.

Integration Demands Alignment

In Momentum Scaling terms, pricing is where your understanding of customer value creation either proves itself or reveals its gaps. If you’ve truly integrated into how your customers create value, if you understand their constraints and their aspirations and their economics, then the right pricing model becomes obvious.

You can’t integrate into a customer’s value chain when your pricing model fights against how they think about value. A subscription model for episodic value creates cognitive dissonance. Project pricing for continuous value makes scaling impossible. Usage-based pricing for fixed-benefit products creates unpredictability that customers hate.

The founders who build momentum understand this instinctively. They don’t ask “What pricing model gets us the best valuation?” They ask, “What pricing model makes it easiest for customers to say yes and hardest for them to walk away?”

The Permission to Price Differently

This is where Permission comes in, that internal authorization to act despite uncertainty and external pressure. Choosing a pricing model that doesn’t follow the standard playbook takes Permission. Your investors want predictable recurring revenue. Your competitors all charge monthly. The SaaS orthodoxy says subscriptions are the only path to scale.

But Permission means looking at what your customers need, not what your pitch deck needs. It means recognizing that subscription fatigue is real, that budgets are under pressure, and that the old models don’t fit every situation.

Some products should be sold as projects, one-time transformations that deliver lasting value. Some should charge based on outcomes or usage, aligning incentives and removing the fixed-cost pressure. Some should be positioned as capital investments that help customers build capability rather than optimize operations they’re already running.

The companies that scale with momentum make these choices deliberately. They understand that pricing is a strategy, not just arithmetic. They know that the right pricing model accelerates customer success, while the wrong one creates drag that no amount of feature development can overcome.

Making the Choice

So how do you decide? Start by understanding where you fit in the customer’s value creation process. Are you helping them optimize daily operations or build lasting capabilities? Is your value continuous or episodic? Does it compound over time or deliver in discrete chunks?

Then look at how your customers budget and buy. Do they think about you as an operating expense or a capital investment? Do they measure your value monthly or over longer horizons? What creates friction in their buying process, and what removes it?

Finally, ask yourself what alignment looks like. Does your pricing model make your success dependent on their success? Or does it create misaligned incentives where you win when they’re locked in, regardless of the value you’re delivering?

These questions don’t have universal answers. But they have specific answers for your business, your customers, your moment. And getting them right matters more than most founders realize.

Because pricing isn’t just about revenue. It’s about the relationship you’re building, the value you’re claiming to create, and whether you’re making it easy or hard for customers to let you into their world.

The default choice, the subscription everyone expects, might be exactly wrong for what you’re building. And recognizing that, having Permission to price differently, might be the strategic clarity that separates momentum from mere growth.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in an unpredictable world by thinking strategically and acting tactically.
Find out more at https://www.davender.com and https://linkedin.com/in/coachdavender .

In early 2003, Mike McDerment was running a four-person web design agency in Toronto. He was using Microsoft Word and Excel to create invoices. Both were slow, clumsy tools that weren’t built for the job. One day, he accidentally saved over an old invoice, losing hours of work.

That frustrating moment changed everything.

McDerment spent the next two weeks coding his own invoicing solution. At first, it was just an internal tool to bill his own clients. But freelancers who saw it asked if they could use it too. Then their colleagues wanted access. What started as a side project solving one designer’s problem was becoming something bigger.

McDerment faced a choice. His design agency was profitable, especially after he restructured it around value-based pricing instead of hourly billing. But he recognized a fundamental ceiling: no matter how smart his pricing, his income was still tied to his time and his team’s capacity. Building websites, even at premium rates, couldn’t scale beyond those constraints.

FreshBooks offered something different: the potential for recurring revenue from a product he could build once and sell many times. Even when FreshBooks was only generating $100 per month after two years — a fraction of what the agency made — McDerment saw the path forward. He loved building products. He was passionate about serving customers who used his software. And he understood that recurring revenue, however modest initially, could compound in ways project-based work never would.

McDerment made a decision that would define the next decade: he would bootstrap FreshBooks without outside capital. He moved into his parents’ basement for three and a half years to save money. In 2004, he worked just 19 days on design work while generating over $200,000 in revenue from the restructured agency. That income became the bootstrap capital for FreshBooks, allowing him to focus most of his time building the product while keeping the lights on.

The early wins felt modest. A few freelancers found the invoicing tool helpful. Some recommended it to colleagues. Word-of-mouth referrals started trickling in. Revenue in 2007 was $255,000. Respectable, but hardly a rocket ship.

But here’s what was happening beneath the surface: a flywheel was building.

Satisfied freelancers told other freelancers. Each user who saved time on invoicing became an advocate. Small service businesses that hated traditional accounting software found precisely what they needed. McDerment and his co-founders, Levi Cooperman and Joe Sawada, kept refining the product based on direct customer feedback, continuously improving core functionalities like expense and time tracking.

By 2010, revenue hit $15.6 million. Still bootstrapped. Still profitable. Still entirely controlled by the founders.

McDerment didn’t take outside investment until 2014, after building FreshBooks to more than 10 million users across 160 countries. By then, he wasn’t raising money to prove the concept or survive. He was raising $30 million to accelerate a business model he’d already validated through a decade of customer revenue and organic growth.

Today, FreshBooks has served more than 30 million people and achieved unicorn status with a valuation exceeding $1 billion.

The flywheel gains momentum gradually at first. Early wins feel hard-fought and uncertain. But as you progress, the compounding effects become undeniable. Each satisfied customer introduces you to new prospects. Each small improvement strengthens your market position. Your growing track record makes the next sale progressively easier to secure.

McDerment built real capabilities, genuine relationships, and earned trust. These advantages compounded over time and became nearly impossible for competitors to replicate by simply spending more money.

Trust the process. Keep delivering value. Your momentum is already starting.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in an unpredictable world by thinking strategically and acting tactically.
Find out more at https://www.davender.com and https://linkedin.com/in/coachdavender .

In 1999, I thought vision was a luxury. Product was what mattered.

Our company’s whole strategy was tied to Y2K. The millennium bug had created genuine anxiety across every industry. Companies were scrambling to audit their systems, update their software, and prepare for a digital apocalypse that might or might not arrive. The market urgency was real. Money was flowing. We had a product that addressed a piece of the puzzle.

What we didn’t have was a voice.

We had hooked our wagon to someone else’s momentum. Y2K wasn’t our idea. The urgency didn’t belong to us. We were positioning ourselves to capture some of the spending that the calendar had made inevitable, hoping customers would discover our utility on their own.

When our execution stumbled, we discovered what that meant. Customers didn’t wait for us to figure things out. They had no loyalty to our approach, no belief in our unique contribution. They had a deadline and a dozen vendors who could help them meet it. We became irrelevant almost overnight.

I’ve thought about that failure for twenty-five years. Recently, reading Daron Acemoglu and Simon Johnson’s prize-winning book Power and Progress, I encountered an idea that named something I had sensed but had no words to describe until now.

Vision Captures Resources

Acemoglu won the 2024 Nobel Prize in Economics for his work on how institutions shape prosperity. In Power and Progress, he and Johnson argue that technological innovation doesn’t follow an inevitable path. It follows whoever has the clearest vision of where it should go.

Throughout history, that clarity has belonged to elites. Medieval agricultural improvements enriched the church and nobility while peasants starved. Early industrialization delivered stagnant wages for workers while factory owners accumulated unprecedented wealth. The gains from innovation flow toward those who control the narrative about what the innovation is for.

Acemoglu puts it directly: vision shapes choices, and power defines whose vision prevails.

This sounds like a critique aimed at billionaires and policymakers. It is. But the same dynamic plays out among startup founders every day. Vision captures resources. Those who articulate how they want the future to evolve are the ones who attract capital, talent, attention, and loyalty. Those who don’t become interchangeable.

The Product Trap

Most founders I work with reflexively hide behind their products.

They believe the product’s utility speaks for itself. They build features, refine user experience, and optimize conversion funnels. They hope potential customers will discover their value proposition through demos, trials, and case studies. The product is the message.

This is a losing strategy in a war for attention.

While you’re waiting for customers to discover your utility, competitors with a clear vision are actively enrolling people in a direction. They’re not just selling a product. They’re articulating a transformation and inviting people to be part of it. Customers aren’t buying hardware, software or services. They’re joining a movement toward something better.

The product-focused founder competes on features. Every competitor can match features. The vision-driven founder competes on meaning. Meaning is much harder to replicate, especially in today’s AI-influenced landscape.

Our Y2K-focused digital health startup had a product. What we lacked was a reason for anyone to care specifically about us. We weren’t leading anyone anywhere. We were a vendor, interchangeable with any other vendor who could meet the same deadline. When we stumbled, customers moved on without a second thought. No relationship transcended the transaction.

Vision as Declaration

Acemoglu’s historical examples show that elites capture gains because they control the narrative. They articulated what innovation was for, and everyone else built within that frame.

Startup founders face the same choice. You either articulate your own vision loudly enough to attract believers, or you get absorbed into someone else’s narrative frame. You become one more vendor serving the current market urgency, whether that’s Y2K compliance, mobile-first design, cloud migration, or AI integration.

The investors funding your growth have a vision for what a successful exit looks like. The platform companies whose systems you depend on have a vision for how you fit into their strategy. The dominant narrative about disruption and scale is a vision about what startups should optimize, and it’s not necessarily in your best interests. If you are not broadcasting your own vision, you’ll find yourself serving these external agendas without ever consciously choosing them.

Vision isn’t a luxury for founders who have time for philosophy. It’s the mechanism by which you claim territory in a noisy world. It’s how you create customers who wait when you stumble, who advocate for you when you’re not in the room, who choose you over cheaper alternatives because they believe in the future you’re creating.

The Cost of Silence

Founders resist articulating a bold vision for understandable reasons. It’s risky. It feels presumptuous. It raises expectations. It can alienate people who don’t share your vision. Safer to let the product speak, to stay flexible, to avoid committing to a transformation you might not be able to deliver.

But silence has its own cost. In a market flooded with capable products, customers default to whoever captures their attention first. They don’t have time to evaluate every vendor’s utility. They look for signals that someone knows where they’re going and can take others along for the journey.

When you hide behind your product, you forfeit that signal. You become discoverable only through features and pricing, competing in a race where someone can always undercut you. You build a customer base with no motivation to stay.

Whose Vision Are You Building?

I couldn’t have articulated this in 1999. We thought urgency was enough. We thought execution separated winners from losers. We didn’t understand that attention flows toward vision, and resources follow attention.

The founders who thrive in the long term aren’t necessarily those with the best products. They’re the ones willing to declare what they’re building toward and why it matters. That declaration becomes a beacon for customers who share the vision, a filter for decisions that might dilute focus, and a foundation for relationships that survive setbacks.

So here’s the question worth sitting with: Are you articulating a vision that captures attention, or are you hiding behind a product and hoping to be discovered?

If customers can’t distinguish your direction from a dozen competitors, you’re building within someone else’s frame. And if Acemoglu is right, you’ll only realize it when the resources flow elsewhere.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in an unpredictable world by thinking strategically and acting tactically.
Find out more at https://www.davender.com and https://linkedin.com/in/coachdavender .

Element AI grew spectacularly. It never scaled. There’s a difference.

In 2017, the Montreal-based AI company closed a $138 million CAD Series A, the largest AI funding round in Canadian history. Co-founder Yoshua Bengio would soon win the Turing Award. Microsoft, Intel, and Nvidia signed partnership agreements. The Québec government threw its full support behind the venture.

Within months, the company opened offices in Toronto, London, Singapore, and Seoul. They launched product initiatives in insurance, manufacturing, and logistics. They raised over $460 million CAD total.

In 2020, ServiceNow acquired Element AI for roughly $310 million CAD, barely two-thirds of the capital raised. Most employees received layoff notices. The brand disappeared.

The Distinction That Matters

Growing adds customers, revenue, headcount, and offices. Scaling transforms what you are. Growth is arithmetic. Scaling is a transformation.

A startup that merely gets bigger remains just as vulnerable as when it was small. It just has more to lose.

Element AI chased multiple markets simultaneously. Insurance one month, manufacturing the next. They accumulated collaborations with impressive names. But collaborations aren’t the same as integration. None of these relationships embedded Element AI into how their partners created value for their own customers.

They had logos on press releases. They didn’t have customers who couldn’t function without them.

Without that deep integration, they remained a vendor, easily replaced when conditions shifted. They built platforms across several industries without becoming indispensable in any of them. Revenue remained minimal. The gap between spending and income widened each quarter, masked by that deep pool of investor capital.

They measured growth: offices opened, partnerships signed, and employees hired. By all appearances, they never measured transformation. Transformation would have changed what they were, not just expanded what they did. It would have embedded them so deeply in a customer’s world that replacing them would have become unthinkable.

What might that have looked like? Picking one vertical. Insurance, perhaps. Embedding their team with a single carrier for eighteen months. Learning how underwriters actually make decisions. Building AI that fits into existing workflows rather than replacing them. Becoming so woven into that carrier’s operations that switching would mean rebuilding entire processes. Then, and only then, expanding to the next customer.

Measure Transformation, Not Growth

You can experience rapid growth while remaining interchangeable, easily displaced when conditions shift. Backward-looking metrics like revenue and headcount tell you what happened. They don’t reveal whether you’ve built sustainable competitive advantages.

True scaling health lies elsewhere. How indispensable have you become to your customers’ success? How high are the switching costs you’ve created? Are you capturing value proportional to the value you deliver?

Element AI had world-class researchers, unlimited capital, and political backing. What if they had raised $15 million instead of $138 million? That constraint would have forced them to pick one vertical, prove value to actual customers, and integrate deeply before expanding. They would have had to earn the right to grow.

The question isn’t whether to grow. The question is whether your growth builds a lasting competitive advantage or merely makes for prolific press releases.

Are you just getting bigger, or transforming into an indispensable partner?

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in an unpredictable world by thinking strategically and acting tactically.
Find out more at https://www.davender.com and https://linkedin.com/in/coachdavender .

It’s January 1st. The calendar has turned. And if you’re like most people, you’re already thinking about what you want to change this year.

Maybe you want to finally launch that startup. Scale the one you’ve got. Leave the job that’s slowly draining you. Build something that matters.

But here’s the question nobody asks: who gave you permission to do any of that?

Not your investors. Not your board. Not your spouse, your parents, or your mentors. Not even me, though I wish I could.

The truth is, no one can give you permission to become who you’re meant to be. You have to find it yourself. And the place to look isn’t forward into some imagined future. It’s backward, into your own history.

How I Found My Permission

In 2000, my startup collapsed.

We were building a digital health venture, raised money, hired a team, and bet everything on a January 1st launch tied to Y2K. When it fell apart, I lost more than my job. I lost my savings, my confidence, and my sense of who I was supposed to be.

In the months that followed, as the tech bubble burst and then the towers fell, I watched people around me spiral into the same crisis I was living. Everyone was questioning everything. Who am I? What am I supposed to do now? Do I have what it takes to start again?

And somewhere in that fog, I wrote a manifesto. A declaration that I would spend my life equipping leaders with “the clarity, commitment, confidence and courage to make a positive difference in the world.”

It sounds grandiose now. At the time, it felt audacious. Who was I to coach founders? I’d just failed spectacularly. I had no credentials, no certification, no external validation whatsoever.

But I had something else. I had evidence.

Not evidence that I was destined for success. Evidence that I had already been doing the work I was claiming permission to do. I just hadn’t recognized it.

The Evidence Was Already There

Years earlier, when I was a newly minted Captain in the Royal Canadian Air Force, I’d been assigned to take over a shop with twenty technicians. One tech in particular had been underperforming for months. The paperwork was already in motion. My commanding officer assigned me to complete the process and release him from the military.

Instead, I started asking questions. What I found wasn’t a bad worker. It was someone whose particular talents were being ignored, whose capabilities were going unused. I found a way to redeploy those talents, and he didn’t just improve. He became a top performer.

I didn’t think much about it at the time. It was just problem-solving. But looking back, that moment revealed something essential about who I was: I could see potential that others had written off, and I could find ways to unlock it.

That wasn’t management. That was coaching, years before I had the word for it.

Then there was the startup itself. Yes, it failed. But something happened during those final months that I almost missed in my grief over the outcome.

When the pressure was highest and the hours were longest, I was there. Not in the executive suite reviewing spreadsheets, but with my team during the overnight grind sessions, making sure they had what they needed to do their work. When everything was falling apart, I didn’t retreat. I stood beside my people.

That revealed something, too. My instinct under pressure wasn’t self-preservation. It was presence. Leadership through standing with people in the hard moments, not above them.

Permission Isn’t Invented. It’s Excavated.

When I wrote that manifesto in the shadow of collapse, I wasn’t inventing a new identity. I was finally recognizing the one that had been there all along.

The Captain who saw potential others missed. The leader who stayed with his team when things got hard. The person whose instinct in crisis was to help others find their way forward.

“I inspire possibility” wasn’t an aspiration. It was a recognition. The evidence had been accumulating for years. I just hadn’t stopped to look at it.

This is what I call permission: the internal authorization to act decisively despite uncertainty. And here’s the thing most people miss: permission doesn’t come from credentials or external validation. It emerges from reflecting on your past successes, something that few of us take time to do.

We’re trained to analyze failures, to conduct post-mortems when things go wrong. But we rarely pause to examine what made our successes possible. When you study moments where you created real value, patterns emerge: the conditions that allowed you to perform at your best, the principles you were honouring, the capabilities you were exercising.

These patterns reveal your authentic foundation for action.

Permission isn’t invented in the moment of crisis. It’s discovered through honest reflection on what has already worked.

Your Permission Is Already There

Your permission works the same way mine did. It already exists, encoded in your history. You just haven’t excavated it yet.

Think about the moments in your life when you created real value. Not when you got lucky. Not when circumstances aligned perfectly. The moments when you made something happen that wouldn’t have happened without your particular combination of skills, instincts, and determination.

What was true about you in those moments? What principles were you honouring, even if you couldn’t have articulated them at the time? What capabilities were you exercising that felt natural, almost effortless?

These aren’t just nice memories. They’re evidence. They’re data points that prove you have done hard things before. And they’re the foundation for your permission to do hard things again.

The Exercise: Finding Your Permission

Here’s what I want you to do today. Not tomorrow. Not next week. Today, while the year is still new and the calendar still feels like a blank page.

Set aside an hour of uninterrupted time. Turn off your phone. Close your laptop. Get a pen and paper, the physical kind.

Then work through these questions, one at a time. Don’t rush. Don’t edit yourself. Just write.

Part One: Mining Your Successes

Think back over your life and identify three to five moments when you created something valuable against the odds. These don’t have to be business successes. They could be from any domain: a relationship you saved, a project you completed despite obstacles, a problem you solved when everyone else had given up.

For each moment, answer these questions:

• What was the situation? What made it difficult?

• What did you do? Be specific about your actions, not just the outcome.

• What strengths were you exercising? What capabilities felt natural to you?

• What principles were you honouring, even unconsciously?

• What did this moment reveal about who you are at your best?

Part Two: Your Permission Statements

Now look at what you’ve written. Patterns will emerge. Themes will repeat. Capabilities will show up across multiple stories.

From these patterns, write ten statements that begin with “I am...” and describe the person who showed up in those moments.

Not “I want to be...” or “I’m trying to become...” but “I am...”

These aren’t aspirations. They’re excavations. You’re not creating a new identity. You’re documenting the one that already exists, but that you’ve forgotten to acknowledge.

Before proceeding, make sure you have at least 10 statements, no fewer. You may need to dig, but you have them within you.

Part Three: Your Empowering Beliefs

From your ten “I am” statements, choose the three that resonate most strongly. The ones that make you sit up a little straighter. The ones that feel both true and slightly uncomfortable to claim.

These three statements are your empowering beliefs. They’re the foundation of your permission. They’re the answer to the question, “Who said you could do this?”

You did. Your own history did. The evidence of your own life did.

Part Four: The Critical Question

Finally, answer this question:

Who is going to give me permission to take the leap I’m ready to make?

Write your answer. Take your time. Be honest.

If your answer is anything other than “I am,” go back through the exercise. Look harder at your successes. Dig deeper into your patterns. Keep writing until you can claim your own permission with confidence.

Why This Matters for Founders

Founders who have a clear sense of who they are approach uncertainty differently. They gather relevant information quickly, filter it through their guiding principles, and act decisively. When those actions don’t produce intended results, they adapt rapidly because they’re committed to staying aligned with their authentic identity while learning from market feedback.

Founders who lack this permission become addicted to analysis. They commission more market research, seek additional customer interviews, always believing that the next piece of information will provide the clarity they need to act. They outsource their decision-making to people who don’t share their mission or bear the consequences of indecision.

Most importantly, those who lack permission end up procrastinating or abandoning their efforts to make the change they say they want.

When you understand who you are and what you stand for, you stop seeking external validation for every choice. You act from authentic clarity rather than reactive anxiety.

In a world of endless possibilities and constant change, your own identity becomes the North Star that keeps you moving in the right direction, even when the path ahead remains unclear.

Your Permission Practice

Developing internal permission requires consistent practice in smaller decisions before crisis moments test your foundation.

In daily decisions, when choosing among competing priorities, ask which option best aligns with your guiding principles.

In strategic choices, before pursuing new opportunities, assess whether they enhance or dilute your authentic identity.

In crisis responses, resist the impulse to abandon who you are. Instead, ask how you can adapt your approach while staying true to your core.

The permission you build through these small moments will be there when you need it most. That’s how it worked for me. When my startup collapsed, I didn’t find my permission in the wreckage. I found it in the patterns I’d already lived. The crisis just forced me to finally look.

Happy New Year

Today, you have a choice.

You can make the same resolutions most people make, powered by willpower that will fade by February. Or you can do the harder work of excavating your permission, documenting the evidence of your own capability, and giving yourself the authority to act.

No one else can give you this permission. But no one can take it away, either. It’s yours, encoded in your history, waiting to be claimed.

The question isn’t whether you’re ready. The question is whether you’re willing to look backward long enough to see the foundation you’ve already built.

Happy New Year. Now go find your permission.

This post draws from Chapter 7 of my forthcoming book, Momentum Scaling, which helps founders navigate uncertainty without losing themselves in the process. If you’re building something that matters and want frameworks for the journey, subscribe to follow along.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in an unpredictable world by thinking strategically and acting tactically.
Find out more at https://www.davender.com and https://linkedin.com/in/coachdavender .

A colleague asked me: “But what about luck? What role does it play in successful scaling?”

Luck as Manufactured Readiness, Not Random Fortune

In a BANI world, the conventional view of luck — being in the right place at the right time — becomes almost meaningless. There’s no reliable way to predict where “the right place” and when “the right time” will be. Your environment is too nonlinear and incomprehensible for that.

BANI describes the emotional and systemic fragility of today’s interconnected, fast-changing and unpredictable world:

• Brittle systems that appear stable until they shatter without warning.

• Anxious environments where decisions are paralyzed by constant uncertainty and psychological strain.

• Nonlinearity, where small inputs produce wildly disproportionate outputs.

• Incomprehensible causality that defeats even expert analysis.

In this world, prediction is a fool’s errand.

This suggests a different definition of luck entirely. Instead of correctly guessing which door to stand in front of, luck becomes having enough doors open that opportunity can find you through one of them.

This strategy, called optionality, transforms luck from a passive state (hoping something happens to you) into an active capability (positioning yourself to capture value when conditions shift).

Founders who “got lucky” during COVID, such as in video conferencing, online learning, and food delivery, didn’t predict those shifts any better than their competitors. They were simply ready when conditions changed. Louis Pasteur’s observation that “chance favours only the prepared mind” takes on an operational meaning here. Preparation isn’t about predicting specific outcomes. It’s about building the capacity to recognize and exploit opportunities faster than competitors who are caught flat-footed.

The Research on Luck and Startup Success

This reframing matters because luck is genuinely structural in entrepreneurship. Studies of founder performance find that “unexplained” variance, or what researchers identify as luck, remains substantial even after controlling for education, experience, and effort. Being slightly early or late to a platform shift, catching a regulatory tailwind, encountering the right investor at the right moment — these factors create massive divergence between companies with similar competence levels.

Hypergrowth case studies tell a consistent story. Slack and Dropbox each encountered fortuitous alignments of distribution channels, cultural trends, and partner ecosystems that accelerated adoption far beyond what their original plans had anticipated. Shopify spent years building e-commerce infrastructure before COVID drove millions of businesses online almost overnight. Was that luck? Certainly, the timing was. But Shopify had manufactured the readiness to capture it—the platform, the merchant tools, the scalable architecture were already in place when the wave hit.

Studies of serendipity in entrepreneurship show that chance events appear in nearly all investigated firms, but only some teams recognize and rapidly exploit them. So yes, luck matters. But acknowledging that luck is structural doesn’t mean surrendering to randomness. It means designing for it.

Why BANI Makes Optionality Essential

Each dimension of a BANI environment reinforces why optionality matters more than prediction.

In Brittle systems, concentrated bets shatter. Optionality distributes your exposure across multiple pathways, so that when one collapses, other options remain viable. The founder who spreads their risk among more than one opportunity captures market share, talent, and customers from competitors whose brittleness has just become their downfall.

Anxiety paralyzes decision-making when the stakes feel binary. (A or B choose one and only one now!) Optionality reduces psychological pressure because no single choice is existential. You can act with informed confidence while competitors wait for certainty that will never come.

Nonlinear dynamics mean that small inputs can produce disproportionate outputs, but you cannot know in advance which inputs are in play. Running many small experiments rather than one large bet increases your chances of catching a favourable nonlinear event. The founder who “got lucky” with their pivot probably had three other pivots ready.

Incomprehensible causality means that even experts misread what’s happening. Rather than trying to understand the unknowable, optionality lets you benefit from surprises regardless of whether you anticipated them or not. You don’t need to predict a specific future if you’ve built systems that can respond to multiple futures.

You Can Make Your Own Luck

Research on entrepreneurial serendipity identifies three practical levers for working with luck.

The first is to expand your surface area for unexpected encounters. Cultivate diverse networks. Run experiments. Create content. Show up in spaces where chance connections happen. Many successful products, partnerships, and business ideas emerge from random encounters rather than linear strategic planning. The founders who seem luckiest have often maximized their exposure to serendipity.

The second is to maintain flexible mental models. When unexpected signals appear, some founders see noise while others see opportunity. The difference lies in cognitive flexibility. A rigid commitment to your original assumptions filters out information that doesn’t fit. Being comfortable with ambiguity lets you recognize when conditions have shifted in your favour.

The third is to give yourself room to manoeuvre by building in some slack in your resources and adaptablility in your plans. Give random positive opportunities enough time to show up. Then, operational agility lets you exploit them before the window closes. The founder with eighteen months of cash and a team that can pivot in weeks will capture opportunities that the founder with three months of runway and rigid processes will miss entirely.

The Skill of Working With Randomness

The link between luck and success isn’t “luck versus skill.” It’s skill at turning randomness into a compounding advantage while surviving negative shocks.

Randomness sets a wide envelope of possible trajectories. Strategy, execution, and leadership maturity determine where inside that envelope your venture ends up and whether it survives long enough to catch favourable waves. Treating standout winners as purely skill-driven outliers ignores the hidden role of luck and risks teaching playbooks that can’t be duplicated under different conditions.

For founders navigating uncertainty, this has practical implications. Stop trying to predict which specific opportunity will materialize. Start building the awareness to recognize opportunities when they appear, the agility to act on them quickly, the clarity to distinguish real opportunities from distractions, and the intention to position yourself where favourable outcomes are more likely.

Luck in scaling isn’t about chance. It’s about having more options than your competitors and the wisdom to exercise the right ones. The question isn’t whether luck matters. It does. The question is whether you’re treating luck as an excuse or as a design parameter.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in an unpredictable world by thinking strategically and acting tactically.
Find out more at https://www.davender.com and https://linkedin.com/in/coachdavender .

In 2008, Tobias Lütke thought Shopify might not survive.

The Ottawa-based founder had built a promising e-commerce platform, but the financial crisis changed everything. Consumer spending collapsed. Major retailers shuttered stores. Lütke watched the news and wondered why anyone would start an online store when people had stopped buying.

Inside Shopify, things were grim. Lütke later described this period as one where the company was struggling badly, when keeping the business alive felt like a daily battle. His team was tiny. Capital was scarce. The downturn created the kind of funding drought and demand shock that kills most young startups. Every decision carried existential weight. They cut costs to the bone and narrowed their focus to the core product, stripping away anything that wasn’t essential.

Lütke braced for the worst.

Instead, Shopify’s growth accelerated dramatically.

The 2008 recession triggered two shifts Lütke hadn’t anticipated. First, mass layoffs pushed unemployed workers to seek alternative income. Traditional employment suddenly felt precarious. Starting an online business became not just attractive but necessary. Second, existing businesses faced intense pressure to cut costs. Companies paying tens of thousands for custom e-commerce solutions couldn’t justify those expenses anymore. Shopify’s modest monthly fees became vastly more appealing.

What made Shopify’s response effective wasn’t luck. It was their ability to recognize what was happening and adapt quickly.

The team watched signup numbers and noticed patterns. New users included laid-off professionals launching consulting businesses, retail workers starting side hustles, and established brands migrating from expensive platforms. Rather than retreating into defensive mode, Shopify leaned into growth.

They focused on making the platform accessible to beginners while keeping it robust enough for established brands. This wasn’t about adding complex features. It was about removing friction. Every barrier to getting a store online represented a potential customer who might give up.

Between 2008 and 2009, Shopify saw measurable increases in both users and paid subscribers. While competitors struggled, Shopify scaled up. By 2010, just four years after launch, they were profitable. The surge in merchants needing immediate solutions allowed the company to grow and generate positive cash flow simultaneously.

Why This Matters

Traditional business planning assumes you can predict the future. You set goals, create milestones, and execute your plan. This works well for established businesses in stable environments. For startups navigating uncertainty, it becomes a trap.

Entrepreneurs who thrive in chaos don’t predict the future. They prepare for multiple futures and build the capacity to respond when conditions change.

Professor Saras Sarasvathy calls this “effectual thinking”. Where traditional planning asks “How do we achieve this specific goal?”, effectual thinking asks “Given what we have and know, what outcomes can we create?”

Shopify’s response in 2008 followed this pattern. Rather than rigidly pursuing pre-recession goals, they observed changing signup patterns, recognized emerging opportunities, and adapted to serve newly urgent customer needs. They worked with what they had to create outcomes they hadn’t planned.

Three factors proved critical. First, their business model aligned with the new economic reality: low upfront costs made sense when everyone faced cash constraints. Second, the platform was genuinely easy to use, even for new users with no technical background. And third, they could scale infrastructure to handle rapid growth without degrading service.

Getting Stronger Under Stress

Most startup advice focuses on building robust businesses that can withstand stress without breaking. But robustness has a dangerous limitation. While you may survive a market shift, you don’t necessarily benefit from it.

Shopify demonstrated something more valuable. They used the crisis to accelerate growth, achieve profitability ahead of schedule, and establish market leadership. The stress made them stronger. Nassim Taleb calls this quality “anti-fragility”: systems that improve because of volatility rather than despite it.

When COVID-19 disrupted commerce in 2020, that organizational muscle served them again. By then, responding to sudden market shifts was part of how they operated. The company that feared extinction in 2008 had learned to treat uncertainty as an opportunity.

The Better Question

When a crisis hits, most founders ask, “How do we survive this?” The better question is “What does this make possible?”

Lütke initially worried about the recession. But he didn’t let fear paralyze the team. He watched what was happening rather than what he feared might happen. When data showed an increase in signups, the team acted on evidence rather than assumptions.

The 2008 crisis didn’t create Shopify’s opportunity. The platform had already solved real problems. But the crisis accelerated market shifts that might have taken years. A relatively small economic disruption cascaded into transformative growth for a company positioned to capture it.

By the time the economy recovered, Shopify had established itself as the leading platform for independent online merchants. Not despite the recession. Because of it.

Founders who build anti-fragile companies aren’t luckier than everyone else. They’re simply paying closer attention.

This post is adapted from an extract of my book, “Momentum Scaling: How To Successfully Grow Your Tech Startup In An Unpredictable World”, specifically from Chapter 6 - Agility: Overcome Your Fragility. I plan to launch this book in the spring of 2026.

Your feedback and comments are welcome.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in an unpredictable world by thinking strategically and acting tactically. Find out more at https://www.davender.com and https://linkedin.com/in/coachdavender .

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Ten years later, Mark Zuckerberg’s “Move fast and break things” is back in fashion. It sounds revolutionary until you remember that every generation believes they’re living through unprecedented change.

As I re-read Jim Collins’ classic Good to Great, this passage made me pause:

“There’s nothing new about being in a new economy. Those who faced the invention of electricity, the telephone, the automobile, the radio, or the transistor — did they feel it was any less of a new economy than we feel today?”

The entrepreneurs who built enduring companies through those transformations didn’t abandon fundamentals because change was accelerating. They doubled down on them.

Yet today’s startup culture treats discipline as the enemy of speed. Founders skip validation, scale before they can execute, and sacrifice profitability for growth — all because the moment supposedly demands it.

When unpredictability increases, rigour becomes more valuable, not less. The companies that thrive through disruption aren’t the ones that cut corners fastest. They’re the ones that build operational foundations strong enough to adapt when conditions shift.

Consider what “moving fast” means when you’re launching features nobody wants or scaling systems that break under pressure.

True speed comes from managing uncertainty rather than ignoring it, turning constraints into advantages, and building execution systems that deliver consistently.

Urgency without discipline isn’t strategy. It’s panic with better marketing.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in an unpredictable world by thinking strategically and acting tactically. Find out more at https://www.davender.com and https://linkedin.com/in/coachdavender .

For four decades, business thinkers have used Warren Bennis’ VUCA framework to describe challenging environments: Volatile, Uncertain, Complex, and Ambiguous. If you’ve attended any leadership seminar, you’ve encountered these terms. They’ve become so familiar they’re practically wallpaper.

But VUCA was coined in a world that now seems almost quaint.

The framework assumes that, with enough analysis, enough scenario planning, and enough strategic foresight, leaders can navigate volatility and reduce ambiguity. It implies that uncertainty is a problem to be solved through better information and sharper thinking.

That assumption no longer holds.

Enter BANI

Futurist Jamais Cascio argues that we’ve crossed a threshold into something fundamentally different. The acronym he proposes is BANI: Brittle, Anxious, Nonlinear, and Incomprehensible. Where VUCA describes external conditions that skilled leaders can manage, BANI captures a world where the very foundations we’ve built our assumptions upon can shatter without warning:

• Brittle describes systems that appear strong but can collapse suddenly and catastrophically. Trade relationships that nations counted on for decades can be dismantled by a single election. Supply chains optimized for efficiency prove brittle when a ship wedges itself in the Suez Canal, or a pandemic shuts down factories. The apparent stability of these systems masked their underlying brittleness. What looks resilient today may be one shock away from failure.

• Anxious captures the emotional toll of living and working in constant uncertainty. The news cycle tears through our consciousness like a tornado, whether we want to pay attention or not. Founders and executives find themselves unable to make confident decisions because the ground keeps shifting. This isn’t ordinary business stress; it’s a pervasive sense that any plan might be obsolete before it’s executed. The anxiety isn’t irrational. It’s a reasonable response to genuinely unreasonable conditions.

• Nonlinear means that causes and effects have decoupled from any predictable relationship. A viral outbreak in one city cascades into a global economic crisis. A new AI model launches and billions of dollars evaporate for some companies while appearing from nowhere for others. Small inputs can produce massive, unpredictable consequences, while enormous efforts sometimes yield nothing at all.

• Incomprehensible goes beyond mere ambiguity. We’re not just confused about what the data means; we’re drowning in data that may be truthy or true, signal or noise, and we lack the capacity to distinguish between them. The complexity of interconnected global systems exceeds human cognitive limits. We can no longer fully understand the environments in which we operate, let alone predict how they’ll behave.

Why This Matters for Founders

I was recently on a call with a founder team that captured all four BANI dimensions at once. Their supply chains were brittle, shattering without warning when shipping costs spiked and tariffs changed overnight. The constant stream of new crises left them anxious, unable to plan with confidence. The relationship between their efforts and their outcomes had become nonlinear: they kept fixing things but couldn’t tell if they were moving forward or running in place. And the sheer complexity of technology evolution, regulatory regimes, talent markets, and shifting client behaviour had become incomprehensible.

They were not failing at strategy. They were drowning in a world that had outgrown conventional strategic thinking.

Their question to me was simple: “How are we supposed to make the right decisions when everything keeps changing?”

The Failure of Traditional Planning

From a young age, we’re taught to dream big and set tangible goals. The SMART framework (Specific, Measurable, Achievable, Relevant, and Time-bound) is the archetype of this approach. Founders are encouraged to set detailed goals, such as a three-year revenue forecast, and then build a plan to achieve them.

This works when the world is predictable, because it relies on three assumptions: linearity (a predictable path from where you are to where you want to be), stability (conditions will remain consistent while you execute), and certainty (you can accurately predict outcomes).

In a BANI landscape, all three assumptions collapse.

Brittleness shatters long-term plans. Anxiety paralyzes decision-making. Nonlinearity invalidates projections. Incomprehensibility obscures measurement. SMART goals create an illusion of control in a world that is fundamentally uncontrollable.

A Different Frame

Here’s what I told that founder team: you operate a fragile startup in a brittle world.

The brittleness is external. Supply chains, trade relationships, market structures, and regulatory environments: these systems can shatter without warning, and you cannot prevent their failure.

But fragility is internal. Your business model, your team, your processes, and your financial resilience: these you can strengthen.

Your job is to make your venture antifragile, able to survive and even thrive when brittle systems shatter around you. Not merely robust, which means surviving stress unchanged, but antifragile: actually growing stronger under pressure.

This requires shifting from destination thinking to direction thinking. Instead of locking yourself into fixed goals that quickly become obsolete, define guiding principles that give you flexibility and a sense of direction. Think of it like using a compass instead of a GPS. You know which way you’re headed, but you’re prepared to navigate around obstacles as you go.

The Stakes Are Higher Than You Think

Consider the pattern: 2000 saw the dot-com bubble burst. 2008 brought the mortgage collapse. 2022 delivered the venture capital contraction and the failure of Silicon Valley Bank. These weren’t random events. They were systemic corrections that punished fragility and rewarded resilience. The interval between them is shortening. The magnitude isn’t decreasing.

When you stack dependencies in your business model, you don’t add risks, you multiply them. A business with six critical dependencies, each with a 90% probability of remaining stable, has only a 53% chance that all six remain stable simultaneously. That’s not resilience. That’s a coin flip.

Extinction-level economic events are not anomalies to prepare for. They’re recurring brittle features of the landscape that must be designed around.

The Path Forward

The founders who survive these corrections aren’t lucky. They’re ready. They build business models that don’t depend on conditions staying favourable. They embrace learning as their primary measure of progress. They create frameworks for decision-making rather than rigid plans that shatter on contact with reality.

VUCA told us the world was challenging but manageable. BANI tells us the truth: the world has become something we cannot fully manage, only navigate.

The question isn’t whether you’re operating in a BANI environment. You are. The question is whether you’re building something that can thrive in it.

This post is an extract from my book, “Momentum Scaling: How To Successfully Grow Your Tech Startup In An Unpredictable World”, specifically from Chapter 1 - The New Reality of Unpredictability. I plan to launch this book in the spring of 2026.

Your feedback and comments are welcome.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in an unpredictable world by thinking strategically and acting tactically. Find out more at https://www.davender.com and https://linkedin.com/in/coachdavender .

When founders hear “ecosystem strategy,” most visualize a Shopify or an Uber, technology platforms with powerful network effects and where each new participant makes the whole system more valuable. The assumption runs deep: if you want to become indispensable in your market, you need to build infrastructure that connects everyone else.

This assumption is expensive. And for most founders, it’s wrong.

There’s another path to ecosystem leadership that requires neither venture capital nor platform economics. It’s called orchestration, and it may be the most underappreciated strategy in entrepreneurship today.

The Platform Paradox

Platform ecosystems work through technology-enabled network effects. Shopify built an infrastructure that lets merchants sell online, then opened APIs so developers could extend functionality. Each new app makes the platform more valuable for merchants. Each new merchant attracts more developers. The flywheel spins faster as scale increases.

The economics are seductive. Once you reach critical mass, platforms scale with near-zero marginal cost. But reaching that critical mass? That’s where the model breaks down for most founders.

Platform strategies require substantial upfront investment in infrastructure before you can demonstrate value. You need enough participants on multiple sides of your marketplace for network effects to kick in. Until then, you’re burning capital while hoping the flywheel eventually starts spinning. This is why platform plays attract venture capital and why venture capital demands platform plays. The model requires patient money willing to fund years of losses before profitability becomes possible.

For founders building with constrained resources, this creates an impossible choice: either raise the capital necessary to pursue platform economics or abandon ecosystem ambitions entirely.

But that’s a false choice.

The Orchestration Alternative

A founder I worked with faced exactly this dilemma when she launched a venture serving families caring for loved ones with dementia. The AgeTech sector was fragmented. Caregivers were overwhelmed, struggling to find reliable products, trustworthy information, and coordinated support. The opportunity for an ecosystem play was obvious.

The platform approach would have meant building technology infrastructure to connect product suppliers, healthcare providers, and caregivers. It would have required significant capital to reach the critical mass where network effects become self-sustaining. She chose differently.

Instead of building a platform, she orchestrated an ecosystem. Rather than creating technology infrastructure for others to plug into, her company became the trusted connector bringing the right partners together at the right time. The company curates products, coordinates relationships, and creates connections that wouldn’t exist without active facilitation.

The results? The venture achieved over 1,500% revenue growth in three years, ranking among Canada’s fastest-growing companies. Not through platform economics. Through relationship orchestration.

How Orchestration Works

Orchestrated ecosystems create value through relationship coordination rather than technology integration. You become indispensable not by owning the infrastructure but by being the one who makes the whole system work better.

This AgeTech company built its ecosystem by first establishing expertise in dementia-friendly solutions. The team carefully curated products and provided expert-driven content to help caregivers navigate overwhelming choices. This positioned the company as the recognized authority that families could trust.

From that foundation, strategic partnerships emerged. The venture secured exclusive Canadian distribution rights for innovative companion products from international suppliers. The founder joined collaborative innovation programmes with research consortia and healthcare networks, participating in work that advances dementia care while building credibility with institutions.

The ecosystem expanded through relationships, not APIs. Suppliers gained access to qualified customers through coordinated marketing. Healthcare professionals discovered reliable resources they could recommend with confidence. Researchers advanced their work through collaborative programmes. Everyone became more successful because this company existed at the centre, orchestrating connections.

Orchestration’s Compound Advantage

Here’s what makes orchestration particularly powerful for resource-constrained founders: the strategy generates value from day one while building toward ecosystem leadership over time.

Platforms require reaching critical mass before network effects provide meaningful returns. Orchestrated ecosystems create immediate value from each relationship and compound that value as the network grows. You don’t need to wait years for the flywheel to start spinning. Each partnership delivers benefits while strengthening your position as the essential connector.

The competitive moats are different, too. Platform moats come from switching costs embedded in technology integration. Orchestration moats come from years of trust and demonstrated value that well-funded competitors cannot replicate quickly. Money can buy technology infrastructure. It cannot buy the relationships that make orchestration work.

This founder’s distribution network illustrates the point perfectly. The trusted channels her company built to serve its ecosystem became the infrastructure for scaling proprietary products internationally. Solutions developed in-house now reach markets across four countries through relationships developed over years of delivering value to partners. This creates diversified revenue while accelerating adoption. A compound advantage that pure product companies or pure platforms cannot easily replicate.

Choosing Your Path

Both platform strategies and orchestration strategies lead to ecosystem leadership. The question is which path fits your circumstances.

Choose platform economics when you can raise substantial capital, when technology integration creates clear network effects, and when you’re willing to sustain losses while building toward critical mass. Choose orchestration when you’re building with constrained resources, when relationships matter more than API connections in your industry, and when you need to generate returns while building toward ecosystem leadership.

Most founders assume they must choose between venture-funded platform plays and remaining a standalone product company. Orchestration offers a third option: building ecosystem leadership incrementally through relationships, generating value at every stage, and creating competitive moats from trust that money cannot buy.

The founders who understand this distinction have a significant advantage. While competitors chase platform economics they cannot afford, orchestrators build something equally powerful through patient relationship development.

Ecosystem leadership isn’t reserved for venture-backed startups with platform economics. It’s available to any founder willing to become the indispensable connector their market needs.

This post is an extract from my book, “Momentum Scaling: How To Successfully Grow Your Tech Startup In An Unpredictable World”, specifically from Chapter 16 - Scaling Your Value”. I plan to launch this book in the spring of 2026.

Your feedback and comments are welcome.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in an unpredictable world by thinking strategically and acting tactically. Find out more at https://www.davender.com and https://linkedin.com/in/coachdavender .

A recent conversation with a founder has been haunting me.

He’s building a business that monetizes social media signals to help creators earn more from their content. His thesis makes sense, at least when he describes it. He communicates a firm grasp of the metrics. His vision to use AI to leverage torrents of social media data to the benefit of creators has a noble ring to it.

But I left that meeting with a sinking feeling.

His business model reminded me of something I’d seen before: the derivatives market that triggered the 2008 mortgage crisis. Brokers celebrated their brilliance while the Jenga towers of collateralized debt obligations that seemed like guaranteed profits in booming markets fell apart when conditions changed. The elaborate structures they built, as chronicled in Michael Lewis’s “The Big Short”, depended on everything continuing exactly as it had been. When the music stopped and, as “Margin Call”’s John Tuld (played by Jeremy Irons) said, “I don’t hear a thing”, that’s when everything screeched to a halt.

In bullish times, sophisticated derivative models can work spectacularly well. But we’re no longer in bullish times.

Three items recently passed through my field of awareness within days of each other, and together they paint a troubling picture that every tech founder needs to consider.

Echoes of 1929

I’ve been reading Andrew Ross Sorkin’s “1929”, which chronicles the days leading up to the Great Wall Street Crash that triggered the Great Depression. The parallels to today are unsettling enough that Sorkin himself has stated, in interviews and in his book, that current market conditions echo many patterns from that era.

The speculative mania back then centred on new technologies like radio. RCA’s stock soared as ordinary investors convinced themselves that this disruptive new technology would reshape the economy. In 2025, we’re seeing identical euphoria around artificial intelligence and related technology stocks. The sense that “this time is different” pervades both eras. The belief that these technologies will create permanent prosperity drowns out concerns about valuation or sustainability.

Both periods witnessed the democratization of finance, placing riskier assets in the hands of individual investors rather than institutions. In the 1920s, margin trading enabled everyday people to take on substantial stock positions with minimal initial investment. Today, the promotion of cryptocurrencies and private equity within retirement savings and pension plans is framed as a means of democratizing wealth creation. However, what is truly being democratized is risk.

The use of leverage, then, as it does today, creates the conditions for cascade failures. When confidence wavers, forced liquidations accelerate the descent. The 1929 crash triggered margin calls, setting off a self-reinforcing spiral. We’re seeing elevated debt levels again: margin debt, corporate leverage, and complex financial instruments that make the system vulnerable to sudden loss of confidence.

One of the issues that concerns me most is the erosion of safeguards put in place after the 1929 stock market crash. During that time, legislators established investor protections and regulatory bodies to prevent another financial disaster. However, many of those protections have now been weakened or dismantled. The relaxation of oversight and the reduction of consumer protection agencies have reintroduced risks that we thought we had successfully contained. As a result, the U.S. economy has become particularly fragile, with negative repercussions for the global economy.

The Big Short 2.0

Then I read about Michael Burry’s decision to close his hedge fund and transition to a family office while placing enormous bets against current market valuations.

Burry is the investor who foresaw the 2008 housing crisis and profited spectacularly by betting against it. His move now carries a stark message about his perception of today’s market. He’s shutting down Scion Asset Management and returning clients’ capital, citing an inability to understand or have conviction about current market behaviour despite continued rallies, particularly in AI stocks.

By converting to a family office, he avoids the scrutiny and disclosure requirements of registered funds. This puts him “off the grid” and free from reporting quarterly trades to the SEC. He can make massive, contrarian bets privately. His fund was known for substantial put option positions against AI equities, suggesting his conviction that a significant correction is coming.

Burry has spoken publicly about accounting gimmicks in big tech and AI firms, warning that profits may be vastly overstated and that sustained rallies could be hiding future depreciation and risks. His closing letter suggested that stepping away is itself a signal: “Sometimes the winning move is not to play”.

When the person who correctly predicted the last major crash takes this position, founders should pay attention.

Beyond Financial Metrics

The third piece came from economist Umair Haque, whose recent Substack argues that the potential for a severe economic crash is both significant and unique compared to typical market cycles.

Haque’s concern centres on something most financial models ignore: political instability as a systemic economic risk. Unlike most crashes triggered by internal economic factors like profits or money flows, he suggests the next crash could be triggered by a far wider range of unpredictable external events, particularly abrupt political decisions that create climates of instability which markets cannot ultimately ignore.

He uses the metaphor of musical chairs to explain that mounting economic and political imbalances make the system ever more precarious. The illusion of endless wealth expansion cannot continue indefinitely as foundational social and political risks escalate. At some point, this multifaceted instability will be “reflected in wealth” through a major correction or a collapse that adjusts values to new socio-political realities.

This framing is crucial for technology founders because it highlights risks that don’t appear in your financial models or market analyses. You can have perfect unit economics and strong product-market fit, yet still be building on a foundation that could fracture from forces entirely outside your control.

The Fragility of Dependency

This stream of thought brings me back to my client and his social media monetization platform. His business model has multiple dependencies, making it inherently fragile.

He depends on continued access to social media platforms whose policies could change overnight. He depends on creator economics remaining attractive despite platform fee structures that could shift and the explosion of AI content. He depends on advertising markets staying robust despite economic headwinds. He depends on AI infrastructure costs staying manageable despite rapid evolution. Most fundamentally, he depends on the continued existence and stability of the platforms themselves.

Each dependency represents a point of failure. When you stack dependencies, you don’t add risks, you multiply them. A business model with six critical dependencies, where each has a 90% probability of remaining stable, has only a 53% chance that all six remain stable simultaneously. That’s not resilience. That’s a 50/50 roll of the dice.

The Antifragile Alternative

Nassim Nicholas Taleb introduced the concept of antifragility: systems that don’t just resist shocks but grow stronger because of them. This stands in contrast to robustness, where the system merely withstands stress, and fragility, when the system breaks under pressure.

The principles of antifragility offer technology founders a framework for building business models that can survive what’s coming. Four principles matter most:

1. Build optionality into everything

Multiple revenue streams reduce dependence on any single customer segment or market condition. Optionality also means reversible decisions that let you change course without catastrophic costs. It means developing capabilities that transfer across markets, so you’re never trapped betting everything on a single opportunity.

Early-stage ventures are inherently fragile because they typically depend on a single revenue stream. When that revenue source dries up, the business can fail. Antifragile business models, on the other hand, create multiple avenues to generate value from core capabilities. For instance, a SaaS company that earns revenue through subscriptions, implementation services, training programs, and data insights can adjust its focus when one revenue stream falters. Similarly, a B2C model that looks beyond just consumer monetization and explores opportunities to become an infrastructure provider can attract interest from retailers and their suppliers, opening up new possibilities. The key is to ensure that these diverse revenue streams share common operational foundations, so you’re not essentially building separate businesses.

2. Embrace volatility as feedback

Instead of treating market turbulence as a threat to overcome, position your business to learn from it and benefit from it. This means executing rapid, low-cost experiments to validate or invalidate assumptions in the moment. These tests might fail, but they protect you from committing to catastrophic preconceptions. Each small experiment that fails eliminates a wrong path and strengthens understanding of what works in the real world. Listen closely for signals in a world full of noise.

This aligns with the Beachhead stage in Momentum Scaling: work with Reference Clients to validate your value creation and capture hypotheses before committing to full-scale operations. Build your value proposition around solving problems that become more acute in times of uncertainty, making your business more valuable precisely when conditions are chaotic.

3. Create compounding advantages

Focus on building a business where each interaction, each operational improvement, each customer success makes the next one easier and more valuable. This means maintaining strategic reserves that provide flexibility to respond to opportunities and threats. It means cultivating customer diversity across segments, industries, or geographies to protect against concentrated risk.

The ultimate compounding advantage is becoming a linchpin at the centre of a value creation network. When multiple parties depend on you to orchestrate value flows between them, you’ve created switching costs that transcend any single relationship. Your customers stay not just because they value what you provide, but because leaving would disrupt an entire ecosystem of relationships and workflows they’ve built around your platform. This network position makes you stronger with each new connection, each integration, each workflow that runs through your systems.

4. Build cash reserves

Financial buffers aren’t inefficient; they’re strategic preparation. When competitors cut back during downturns, you can invest in talent acquisition, customer acquisition, or technology development at favourable terms. The 12 to 18-month cash reserve recommended during early scaling stages isn’t just a safety net. It’s the resource that lets you act when others are paralyzed by uncertainty.

What This Means for Your Business Model

If you’re building on dependencies you can’t control, you’re building on sand. The combination of speculative market conditions similar to those in 1929, credible warnings from investors who foresaw the last crash, and increasing political and economic instability creates an environment where fragile business models face an existential risk. This risk is so significant that no amount of investor capital, even if you’re able to raise it, can offer protection.

Ask yourself these questions about your business model:

Can you survive if your primary revenue source disappears tomorrow? If the answer is no, you have a single point of failure.

Do you depend on platforms, partners, or market conditions outside your control? Each dependency multiplies your risk.

Are you building capabilities that remain valuable even if your initial market fails? If your skills and assets are specific to one narrow opportunity, you’re betting everything on that opportunity not suddenly vanishing.

Does your business become more valuable when conditions are uncertain? If you only thrive in stable, predictable markets, you’re building for a world that no longer exists.

Can you pivot without destroying your financial foundation? If changing direction means starting over financially, you lack the optionality that enables survival.

The answers to these questions reveal whether you’re building something antifragile or something that’s one shock away from collapse.

When, Not If

Consider the pattern: 2000 saw the dot-com bubble burst, destroying companies that had raised hundreds of millions of dollars. 2008 brought the real estate collapse that froze commercial lending overnight. 2022 delivered the venture capital contraction and the failure of Silicon Valley Bank, which exposed how many startups across the US and Canada had confused access to capital with viable business models.

These weren’t random events. They were systemic corrections that punished fragility and rewarded resilience. The interval between them is shortening. The magnitude isn’t decreasing — it’s amplifying.

Extinction-level economic events are not anomalies to prepare for. They’re recurring features of the landscape that must be foreseen and designed around. The founders who survive aren’t lucky: they are ready. They build business models that don’t depend on conditions staying favourable.

The Path Forward

I don’t want to seem as if I am encouraging you to abandon ambition or to stop building. It’s a call to build differently.

The venture capital playbook encourages founders to project confidence about the future, to create detailed forecasts stretching years into the future, and to commit fully to a single vision of how the market will evolve. That playbook was designed for a different world.

In an environment marked by the kind of instability we’re seeing now, the winning strategy isn’t to correctly predict the future. It’s building a business model grounded in value creation, that can thrive regardless of which future arrives.

This means favouring customer-funded growth over investor-funded growth because customers are interested in the value you deliver today, not promises about tomorrow. It means building profitability into your model from the start because profitable businesses have the freedom to adapt while money-losing businesses must surrender to their burn rate.

It means expanding into adjacent markets thoughtfully rather than pursuing explosive growth in a single direction because diversification provides both learning and protection. It means developing deep operational capabilities that compound over time rather than racing to scale before you’ve figured out how to execute consistently.

Most fundamentally, it means accepting that we’re operating in an era where the only certainty is uncertainty. The business models that survive won’t be those that predict the future correctly. They’ll be the ones that build enough optionality, embrace enough volatility as feedback, and create enough compounding advantages that they can adapt in an agile way to — and benefit from — whatever future comes to be.

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The echoes of 1929 are getting louder. Michael Burry is betting against the market with the kind of conviction that preceded his correct call on 2008. Political and economic instability is mounting in ways that traditional financial models can’t capture.

Most founders will miss the opportunity this presents: while your competitors scramble to survive the turbulence ahead, you could be building the business model that thrives because of it.

Antifragility isn’t just defensive. It’s your next competitive advantage.

The founders who recognize this today will be the ones still standing when the dust settles.

Davender’s passion is to guide innovative entrepreneurs in developing the clarity, commitment, confidence and courage to enter, engage and lead their markets in an unpredictable world by thinking strategically and acting tactically. Find out more at https://www.davender.com and https://linkedin.com/in/coachdavender .