Tip of the Spear

How AI Is Rewriting SaaS Economics

October 23, 2025 By Tip of the Spear

The Point: SaaS economics has typically followed a predictable formula: acquire users, expand seat licenses, drive net dollar retention, and compound recurring revenue. That model is now under structural pressure thanks to AI. Artificial intelligence (AI) is changing how software creates value, how customers consume it, and how revenue is captured. The economic engine behind SaaS is being rewritten in real time—and legacy pricing and revenue models are already falling behind.

The SaaS industry has reached a turning point. AI is no longer a feature; it is an economic force to be reckoned with. The software companies that fail to redesign their revenue architecture around AI will see a widening gap between product cost and monetization, resulting in gross margin compression and stalled net retention. Those that act decisively now will build durable revenue advantages and long-term enterprise value.

In this article, I’m exploring how AI is altering the economic structure of SaaS—not just product roadmaps. We’ll examine why traditional seat-based pricing is breaking down, how value is migrating to workflow and outcome-based models, and what this shift means for margins, ARR predictability, and revenue expansion. More importantly, we’ll outline the new rules of SaaS monetization—how to align pricing with value, protect gross margins from AI-driven compute costs, and build a revenue architecture designed for expansion in the AI era… Enjoy!

AI Is Collapsing Legacy SaaS Unit Economics

Traditional SaaS revenue is built on a simple assumption: value is proportional to the number of users who access the product. AI breaks that logic. AI-enabled systems do not simply enable human work—they perform work. Value is shifting away from access and toward output: documents analyzed, code generated, leads qualified, transactions reconciled.

This shift has two economic consequences:

Work replaces users as the value driver. In AI-powered products, usage intensity matters more than license count.
Costs are no longer near-zero marginal. AI inference and compute introduce visible cost of goods sold. Gross margins tighten unless monetization evolves.

If revenue stays tied to seat-based pricing while AI workloads scale, SaaS companies face a structural squeeze: higher operating costs without corresponding revenue capture. This is already visible in earnings calls where AI “feature launches” fail to show material revenue impact.

Why Seat-Based Pricing Is No Longer Enough

The subscription model is not dead, but it is no longer sufficient on its own. Seat-based revenue assumes value is unlocked by giving humans access to a tool. But when AI is performing the work, value is created through action and autonomy—not logins.

This is why the industry is moving toward hybrid monetization models that combine a platform subscription with consumption-based AI pricing. This evolution accomplishes three key economic goals:

Aligns price with value — AI revenue scales with customer outcomes
Protects gross margin — usage pricing offsets inference cost
Drives expansion — usage can grow faster than seat count

This is not theory. Cloud computing went through the same transformation a decade ago—consumption-based models unlocked revenue expansion and hardened defensibility. AI is now accelerating a similar shift in SaaS.

Revenue Must Be Rebuilt Around Value-Captured Economics

Most SaaS companies are still pricing AI the wrong way—either buried as a bundled feature or sold as a premium add-on with arbitrary uplift. Both approaches disconnect revenue from the true value AI creates.

Instead, AI monetization must be tied to value meters—pricing linked directly to measurable output or workflow impact. Examples of value meters include:

Contracts processed (legal tech)
Transactions reconciled (fintech)
Alerts triaged (cybersecurity)
Campaigns generated (marketing)
Tasks automated (horizontal AI platforms)

The strategic question for CEOs is not whether to shift monetization—but where to meter value. At the action level? Workflow level? Business outcome level? The decision defines long-term revenue scalability.

Real Example: How One Company Rebuilt Around AI Consumption Economics

A mid-market compliance software provider faced a pricing challenge. It introduced an AI agent that could automatically review and categorize policy documents. Usage surged—but so did inference costs, eroding gross margins by 14 percent. Seat-based pricing was misaligned with AI workload volume.

The company redesigned monetization:

Base SaaS access: $30,000 annual platform fee
AI consumption: Priced per document processed, with tiered volume levels
Value metric: “Compliance documents analyzed autonomously”

The economic impact:

Net dollar retention increased from 112% to 137%
Gross margin stabilized above 74%
Expansion revenue came from AI usage—not headcount growth

The lesson: businesses that shift monetization from users to workflows will out-scale seat-locked competitors and protect margin in an AI-driven market.

AI Economics Reshape the Entire Revenue Architecture

AI monetization is not a pricing project—it is a revenue architecture redesign. It requires shifts across product, revenue operations, finance, and go-to-market.

Area	AI Economic Impact
Product	Must define value meters and usage telemetry
Pricing	Shift to platform + usage + tiered value packaging
Revenue Operations	Activate consumption growth, not just license sales
GTM Motion	Comp plans must reward usage adoption and workflow expansion
Finance Model	Move beyond ARR to include Consumption Revenue, AI Gross Margin, and Usage-Based NRR
Investor Narrative	Communicate long-term value of consumption expansion to avoid misinterpretation as revenue volatility

Leadership teams that treat AI pricing as a “feature SKU update” will fall behind. This is an operating model evolution.

Strategy Before Features—and Pricing Before Product

AI creates two types of SaaS companies: those who build AI features and hope revenue follows, and those who architect monetization first and build product around unit economics. The second group will dominate the next decade.

To lead in AI economics, CEOs should drive five immediate actions:

1. Define the AI Value Thesis

Determine where AI drives measurable economic impact—speed, accuracy, cost reduction, throughput.

2. Select the Right Value Meter

Tie pricing to value delivered per workflow, transaction, or autonomous task.

3. Introduce Hybrid Monetization

Anchor revenue in platform subscriptions while capturing AI usage expansion.

4. Protect Gross Margin

Track AI cost per workflow and enforce margin targets with tiered usage pricing.

5. Build a Usage-Based GTM Model

Shift sales comp and customer success toward consumption activation and workflow ownership.

Operator-Level Takeaways

AI destroys the assumption that SaaS value equals seat licenses.
Revenue must shift from access to output to capture value created by AI.
Consumption pricing is not an experiment—it’s an economic evolution.
Companies that wait for “pricing clarity” will be priced by competitors.
Investors will reward those who deliver expanding usage economics with protected margins.

AI is not just changing software—it is changing software economics. The companies that act now will define the next era of enterprise value creation.

Sam Palazzolo
Real Strategies. Real Results.

PS – Subscribe to my Business Scaling Newsletter for weekly operator frameworks and AI strategy tools that drive execution.

Sam Palazzolo's How AI Is Rewriting SaaS Economics

The AI Leadership Popularity Contest

September 25, 2025 By Tip of the Spear

The POINT: Welcome to the AI Leadership Popularity Contest — where every leader must decide: do you want to be popular or respected? In the algorithm-driven workplace, your likability might earn quick applause, but only respect earns you the trust that sustains influence. The contest is on — and the stakes are higher than ever!

Welcome to the AI Leadership Popularity Contest!

Every leader eventually faces the same question: Would you rather be popular or respected?

In the past, this was a philosophical debate. Today, it’s a contest with very real stakes. Artificial intelligence (AI) has turned every hiring decision, pricing model, and customer interaction into a popularity vote on your leadership.

Being popular may earn applause.
Being respected earns trust.
And in the age of AI, trust is the only way to win.

Welcome to the AI Leadership Popularity Contest!

The Only Way to Win the Contest: Trust

The modern workforce doesn’t just follow leaders — they scrutinize them. Add AI into the mix, and every decision comes under a brighter spotlight. Employees and customers don’t only want to know what the algorithm said; they want to know why you chose to act on it.

A liked leader hides behind AI: “That’s just what the system recommended.”
A respected leader steps forward: “Here’s why we designed the model this way, and here’s how I’m accountable for its outcomes.”

Trust is the deciding factor. Without it, the popularity contest is over before it begins.

Why Likability Gets Votes, But Respect Wins Elections

Think of likability as the campaign trail — handshakes, smiles, and soundbites. In leadership, that’s being approachable, pleasant, and easy to work with. It’s valuable, but fleeting.

Respect, on the other hand, is what wins the election. It’s built on competence, consistency, and character. It’s the infrastructure behind the campaign: reliable systems, ethical decisions, and results that last.

In AI leadership, likability is the friendly chatbot. Respect is the secure, bias-audited, well-governed system behind it. One might charm you in the short term. The other sustains your credibility long after the contest ends.

The Perils of Leading for Applause

Leaders who chase likability often avoid the uncomfortable. They’ll fast-track AI pilots without governance, roll out shiny automation tools without transparency, or dodge hard conversations about bias and job displacement.

These moves may win quick cheers — like handing out candy at a campaign rally. But when the first scandal hits, applause turns into scrutiny. Employees and customers remember whether you built your campaign on charisma or character.

Respect means making the hard calls, even when they cost you short-term popularity.

Respect Anchors Balance When the Stakes Are Higher

The best leaders know balance matters: you need approachability and accountability. But AI tilts the contest.

Why? Because AI amplifies consequences. A bad hiring decision made by a human affects one role. A biased AI hiring model poisons the entire talent pipeline. A flawed algorithm in lending, insurance, or healthcare can damage thousands of lives — and reputations.

That’s why respect isn’t optional anymore. Employees will forgive a leader who isn’t everyone’s friend. They won’t forgive one who hides behind machines or fails to safeguard them. Respect anchors the balance when the spotlight is on.

Consistency: The Winning Campaign Strategy

Campaign promises are meaningless if they don’t match actions. Leadership works the same way.

Consistency is how respect compounds. If you say you’ll be transparent about AI governance, then show your work. If you say you’ll let data drive decisions, don’t override models when they’re inconvenient. If you say innovation matters, don’t block automation out of fear.

Inconsistent leaders lose elections — and teams. Consistent leaders build trust that no algorithm can shake.

Respect Is the Only Title That Lasts

Technology moves fast. Today’s AI is tomorrow’s legacy code. But respect outlasts the hype cycle.

A leader who earns respect through clarity, accountability, and integrity creates a permanent leadership asset. Employees follow you not because the system told them to, but because they believe in your judgment. Customers stay loyal not because of your latest app, but because they know your values don’t change with the algorithm.

Likability fades with trends. Respect wins the office — and keeps it.

The AI Leadership Imperative

The AI Leadership Popularity Contest is here, whether you like it or not! Every decision you make — human or machine-assisted — casts a vote in your favor or against you.

If you want to thrive in this new era, remember:

Likability gets you applause.
Respect earns you trust.
Trust wins the contest.

Leadership isn’t a Popularity Contest (but it isn’t an Unpopularity Contest either!)

Sam Palazzolo

If you like this, you’ll love my weekly Newsletter… Subscribe here: https://sampalazzolo.com/

From Confusion to Clarity: AI Adoption Strategies

September 9, 2025 By Tip of the Spear

Imagine two family-owned businesses standing at the same crossroads. Both had weathered decades of industry shifts—economic downturns, globalization, digital marketing. Yet, in 2024, when artificial intelligence emerged as the defining force of modern business, their paths diverged.

The first company dismissed AI as “too complicated” and stuck to manual workflows. Their leaders told themselves they’d wait until the technology matured. Twelve months later, competitors who embraced automation were servicing clients faster, predicting demand with accuracy, and reinvesting efficiency gains into growth. The laggard found itself scrambling just to maintain market share.

The second company made a different choice. They recognized that 42% of family-owned enterprises now see AI adoption as their top strategic priority—a Deloitte Private survey finding that should make every leader pause. They didn’t overhaul everything overnight, but they invested in targeted automation, empowered their teams with accessible tools, and added board-level expertise in emerging tech (something 66% of businesses are already doing). The payoff was immediate: better decisions, streamlined processes, and a culture that saw AI as an ally, not a threat.

This is the reality facing leaders today: The future won’t belong to businesses that wait for AI to become simple. It will belong to those who adopt with clarity, purpose, and strategy.

Why AI Is Now a Strategic Imperative for SMBs

Artificial intelligence is no longer confined to Fortune 500 enterprises. Cloud platforms and modular technologies have lowered entry barriers, making it possible for SMBs to apply AI in practical, cost-effective ways.

The Deloitte survey underscores this urgency: family businesses are prioritizing AI above even general technology investments. Why? Because the opportunity is no longer hypothetical. AI is already reducing costs, improving accuracy, and expanding customer engagement in ways traditional systems cannot match.

Failing to act risks more than stagnation—it risks irrelevance.

Practical AI Adoption Strategies for SMBs

1. Start Small, Automate the Mundane

One of the simplest entry points into AI is automating repetitive administrative tasks: invoice processing, HR onboarding, or logistics scheduling. Intelligent Document Processing (IDP) tools can capture, validate, and route data with greater speed and accuracy than manual staff work.

By reducing error-prone, time-consuming chores, SMBs free up talent for higher-value activities like customer engagement, strategic planning, and product innovation. This is not theory—it’s happening today in logistics, healthcare, and professional services.

2. Invest in Board-Level Expertise

The survey finding that 66% of family businesses are adding tech-savvy board members is not a coincidence. SMBs often lack the internal expertise to evaluate AI tools critically. Bringing in advisors or directors with real experience in emerging technologies creates accountability, ensures investments are aligned with business strategy, and demystifies the jargon.

Without this expertise, SMB leaders risk buying into hype—or worse, paralysis by analysis. With it, they create a governance structure that sees AI for what it is: a lever for growth.

3. Bridge the Physical-Digital Divide

Many SMBs still rely heavily on paper—contracts, invoices, shipping documents. Optical Character Recognition (OCR) and scanning technology integrated with AI can digitize this backlog, making information searchable, compliant, and ready for advanced analytics.

This is foundational. Without accessible digital data, higher-order applications like predictive analytics, AI-driven customer engagement, or financial forecasting will underperform. In short: digitization is not the end goal, but it is the necessary first step toward intelligence.

4. Adopt Adaptive Intelligence, Not Just Digitization

Too many SMBs stop at “going digital.” The real competitive edge comes when AI shifts operations from reactive to proactive.

Consider embedding AI-powered analytics into your sales pipeline. Instead of waiting for a quarterly report, leaders can view real-time insights: which customers are at risk, which products are surging in demand, where pricing strategy can shift to protect margins.

Adaptive intelligence doesn’t just streamline operations; it changes how leaders think about growth, risk, and opportunity.

5. Choose Accessible, Purpose-Built Tools

Enterprise AI platforms are often expensive and over-engineered for SMB needs. Fortunately, vendors are now offering tools designed for smaller budgets and non-technical teams.

No-code automation platforms, AI-powered CRMs, and workflow assistants allow employees to build solutions without a data science degree. The best strategy is not to chase “the most powerful AI,” but to adopt the right AI for the right problem—accessible, scalable, and tied directly to business outcomes.

Transitioning from Hype to Action

AI is surrounded by hype, but SMBs can’t afford to dismiss it as a buzzword. The reality is that early adopters are already gaining measurable advantages—reduced costs, faster operations, stronger customer relationships.

The lesson from Deloitte’s research is clear: SMBs that approach AI with clarity and purpose outperform those that wait for perfect conditions. The tools exist today, and they don’t require enterprise budgets or technical armies. What they do require is leadership willing to align adoption with strategy.

Conclusion: The Cost of Waiting

SMB leaders are no strangers to risk. But in the case of AI, the greater risk lies in inaction. Competitors aren’t waiting for AI to become simple—they are already experimenting, already scaling, already embedding intelligence into their workflows.

The path forward doesn’t demand perfection; it demands movement. Start small by automating the mundane. Strengthen governance with tech-savvy expertise. Bridge your physical-digital divide. And above all, adopt tools with purpose.

Because the question is no longer whether SMBs should adopt AI. The question is: will your business be among the 42% who make AI a top strategic priority—or among those left wondering how the future passed them by?

Sam Palazzolo, Managing Director

The AI-First Organization: Redefining Workflows, Talent, and Leadership for the Next Era

August 22, 2025 By Tip of the Spear

Generative AI has moved beyond the experimentation phase. It is no longer a side project or an optional pilot—it is a defining capability that is reshaping the way organizations operate. Yet while adoption is accelerating, impact has lagged. Many leaders are asking: how do we translate widespread AI use into sustainable, measurable transformation?

The answer lies in building what I call the AI-First Organization. Rather than bolting AI onto existing processes, an AI-First Organization integrates generative AI into the core of strategy, workflows, and decision-making. It means asking at every turn: “How can AI inform, accelerate, or transform this work?”

This article explores how to build an AI-First Organization—one that redefines workflows, elevates talent, builds trust through governance, and positions itself for lasting advantage.

From Adoption to Advantage

Today, two-thirds of companies report using generative AI, but very few have unlocked its true potential. Adoption without integration rarely produces results. Consider two contrasting examples:

A global bank rolled out AI copilots across its back office. Initial productivity surged, but gains quickly eroded because workflows stayed rooted in old processes.
A brokerage firm, by contrast, embedded generative AI into its advisor workflows with rigorous oversight. The result: 98% adoption and meaningful gains in efficiency and client service.

The distinction highlights a key truth: the AI-First Organization doesn’t “add tools”—it reimagines how work gets done.

Employees Are Already AI-First—Leaders Must Catch Up

One of the most surprising dynamics is that employees are often ahead of their leaders. Studies show they are using AI tools three times more than executives realize. This shadow adoption represents both a risk and an opportunity.

Left unmanaged, it can expose organizations to compliance and security vulnerabilities. But when harnessed, it can transform employees into co-creators of new workflows and innovations. In fact, companies that engage at least 7% of their workforce in transformation initiatives are twice as likely to outperform their peers on shareholder returns.

In an AI-First Organization, employees are not passive users. They are active participants—building agents, piloting workflows, and sharing learnings across teams.

The North Star of an AI-First Organization

Every transformation needs a North Star. For an AI-First Organization, that vision must be anchored in outcomes, not tools. The critical question is not “Where can we add AI?” but “What outcomes do we want AI to unlock?”

Examples include:

In healthcare, AI-driven patient engagement reducing readmissions by 15%.
In retail, AI-powered merchandising cutting inventory holding costs by 20%.
In professional services, AI knowledge copilots halving the onboarding time for new hires.

This shift in framing—from tool deployment to outcome orientation—helps align leadership, technology, and talent toward the same strategic direction.

Trust as the Foundation of AI-First Work

The AI-First Organization is built on trust. Employees must trust the outputs, customers must trust the service, and regulators must trust the governance. Without it, adoption stalls.

Core Components of Trust-Centric AI

Human-in-the-loop oversight for critical workflows.
Bias and hallucination controls to safeguard accuracy.
Cross-functional governance bodies ensuring ethical use.
Transparent evaluation and communication so employees understand how models are validated.

Morgan Stanley’s success underscores the point: trust, not technology alone, drives enterprise-wide adoption.

Redesigning Workflows: From Copilots to MVOs

Becoming AI-First means rebuilding workflows from the ground up. The path typically unfolds in three stages:

Phased Workflow Evolution

Assisted Workflows: Humans supported by AI copilots (e.g., marketers drafting campaigns with AI).
Agent Groups: Semi-autonomous clusters of agents coordinating tasks (e.g., finance teams running forecasting scenarios).
Minimum Viable Organizations (MVOs): Fully autonomous agent-driven units running lean business functions (e.g., customer service handled primarily by AI, with human escalation only when necessary).

This staged progression ensures organizations scale AI integration responsibly while capturing increasing levels of efficiency.

Redefining Talent for the AI-First Organization

The organizational model must also evolve. Some business units will become lean AI-led MVOs. Others—particularly those requiring human judgment and empathy—will remain human-led but AI-augmented. This requires a fresh talent strategy.

Talent Roles of the Future

AI Workflow Optimizers: Professionals redesigning processes around AI capabilities.
Automation Product Owners: Leaders accountable for scaling AI-enabled systems.
Reskilled Frontline Talent: Employees shifted toward creativity, relationship-building, and problem-solving—skills AI cannot replicate.

An AI-First Organization invests as much in reskilling talent as it does in new technology.

Employees as the Catalyst of Change

Top-down mandates alone will not build an AI-First Organization. Transformation accelerates when employees are empowered as change agents.

McKinsey’s own internal “Lilli” platform illustrates this: 17,000 employees created their own AI agents, leading to 92% usage. Similar results are possible when employees are encouraged to experiment, build, and share.

The principle is simple: participation drives adoption, and adoption drives results.

Closing Thoughts: Leading the AI-First Future

Generative AI is not just another tool—it is a structural shift in how organizations configure work, allocate talent, and generate value. Building an AI-First Organization requires:

Reimagining workflows, not just deploying tools.
Redefining talent strategies for AI-enabled roles.
Building trust through governance and transparency.
Empowering employees as co-creators of change.

Leaders who embrace this mindset will scale smarter, faster, and more resiliently than those who remain in the experimentation stage. The AI-First Organization is not the future—it is already here.

For executives committed to scaling in this new era, now is the time to act. Define your North Star, empower your people, and build trust into the system. That is how organizations turn generative AI into lasting competitive advantage.

Sam Palazzolo

If you want practical insights on scaling strategies and leadership in the AI era, I invite you to sign up for my Business Scaling newsletter at sampalazzolo.com

Customer Funding: Venture Funding’s Overlooked Option

July 31, 2025 By Tip of the Spear

For decades, the default growth story has been simple:
Raise more money. Venture capitalists back the big idea. Banks extend credit. Balance sheets swell with other people’s capital.

But this binary view—equity or debt—comes at a cost. It assumes that outside capital is the only fuel for growth. For many companies, especially those looking to scale beyond the early stage, the result is dilution, debt, and distraction.

There is a third way forward. Customer funding—still underutilized even among experienced leaders—is emerging as a viable, and often faster, growth engine.

The Problem with the Two-Legged Stool

Research from McKinsey & Company (Strategy: Beyond the Hockey Stick) underscores how difficult it is to achieve breakout growth: fewer than 1 in 12 companies move up a performance tier in a decade. One contributing factor is that the pursuit of external funding becomes an end in itself.

Time and energy that could be spent deepening relationships with customers instead gets poured into pitch decks, investor roadshows, and loan negotiations. Equity can provide time and capital for bold moves, but at the cost of ownership. Debt can amplify returns, but adds pressure and risk. Both create dependencies on decision-makers outside the company’s walls.

It leaves too many leaders sitting on a two-legged stool—unstable, waiting for someone else to believe in them enough to fund their next move.

The Third Leg

Customer funding adds the missing third leg.
Instead of relying solely on outsiders, you let customers fund growth directly. They do this when they pre-pay, subscribe, commit early, or purchase services that finance the creation of future products.

John Mullins, a professor at London Business School and author of The Customer-Funded Business, has documented how companies as different as Airbnb, Dell, and countless service firms have built expansion on customer cash rather than investor dollars. This approach doesn’t remove the need for outside funding. It simply changes the order of operations: customers first, capital later.

How Customer Funding Works

Customer funding isn’t a single tactic. It’s a mindset—one that expresses itself in several proven models.

Platforms like Airbnb and Uber show the matchmaker model: connecting buyers and sellers, taking a fee, and scaling without ever owning inventory.
Companies like Dell demonstrate pay-in-advance models, securing revenue before building through pre-orders or direct-to-consumer commitments.

Many organizations opt for a subscription model, transforming one-off sales into recurring revenue streams that create predictable cash flow. Software-as-a-service businesses are the classic example here.

Another path begins with a service-to-product approach. High-value, bespoke services generate income that funds the development of standardized, scalable products. Consulting firms that evolve into software companies follow this trajectory.

Finally, time-and-materials contracts allow agencies, consultancies, and specialized manufacturers to fund their capability building and growth as client work progresses.

The unifying principle is simple: the customer relationship becomes the funding source.

Why This Approach Matters Now

Conditions for growth have shifted. Capital is more expensive. Investors are slower to commit, valuations are lower, and lenders are scrutinizing cash flows more than ever. Meanwhile, competitive pressures have only intensified.

In this environment, customer funding delivers two powerful advantages:

Speed: Pre-orders, subscriptions, and bundled service contracts create immediate access to cash, allowing companies to act faster than those waiting for a round to close.
Focus: When customers pay upfront, what gets built aligns tightly with market demand. It is real-time validation that external funding alone can’t replicate.

Integrating Customer Funding into a Broader Strategy

Customer funding is not an argument against external capital. The best operators use all three levers—equity, debt, and customer funding—to complement each other. Starting with customers, however, de-risks the business.

When you arrive at the table with proof of demand and revenue in hand, you negotiate from a position of strength. Investors and lenders respond differently when the market has already validated your path.

For leaders exploring this approach, ask yourself:

Can our best customers be persuaded to commit earlier, even pre-paying?
Could we offer a recurring option that creates more predictable cash flow for both sides?
Could a service we deliver today evolve into a product that scales tomorrow?

These questions reframe growth as something that begins inside the business, not outside of it.

A Different Kind of Growth Mindset

For years, “growth mindset” has been shorthand for raising another round. But durable, controlled growth rests on a stronger foundation.

Equity buys time. Debt buys leverage. Customer funding buys freedom.

As competition intensifies and capital tightens, more leaders are discovering that the most dependable source of growth capital may be the customers they already serve. When those customers become partners in funding the next stage, the question changes from “Who will give us the money?” to “How fast can we deliver?”

Sam Palazzolo