Part 3: State of play and the right company for you
Picture this: You're offered a sales role at a hot database startup. They've raised over $100 million. They have cutting-edge technology that developers absolutely love. The founders are technical visionaries. The growth metrics look incredible. So you take the job. Fast forward eighteen months later, and you're watching the company get acquired in what the press calls a strategic partnership, but what you now realize was actually a fire sale to avoid bankruptcy.
This isn't a hypothetical scenario. This is exactly what just happened to Neon, and it's a story every tech sales professional needs to understand.
Let me tell you about Neon. On paper, they had everything going for them. Serverless PostgreSQL that solved real problems for developers. Instant database branching that made preview environments trivial. Technical innovation that genuinely impressed the engineering community. They raised $129.6 million dollars. They had 750,000 databases under management. They were deploying 3,000 new databases every single day. They had 130 employees working on cutting-edge infrastructure. And they couldn't figure out how to make money.
Think about that for a second. All that innovation, all that funding, all that growth, and the business model simply didn't work. When Databricks acquired them, it wasn't because Neon was successful. It was because Databricks needed to solve a strategic problem, and buying Neon was cheaper than building the solution themselves.
Now, if you're in sales, this presents a particularly dangerous situation. Engineers can look at Neon's architecture and evaluate its technical merit. Finance people can immediately spot the unit economics problems. But as a sales professional, you're often forced to trust that someone else has figured out the business model fundamentals. And that trust can be incredibly expensive.
Here's what made Neon especially treacherous: The product actually worked. Developers loved it. The adoption metrics were impressive. The technical community embraced the innovation. But here's the problem, all of that enthusiasm was coming from users who were paying exactly zero dollars per month. Even worse, AI code generators were creating four times more databases than actual humans. So their growth was increasingly driven by automated systems that would never, ever convert to paying customers.
This brings us to a critical point about the current AI boom. AI is amplifying both the opportunities and the risks in infrastructure startups. Companies like Neon can achieve massive scale because AI tools automatically default to their platforms. This creates incredible usage statistics and compelling growth narratives. But it also means their user base is increasingly dominated by systems that will never pay for the service.
As a sales professional, you need to understand that user growth driven by AI automation can look exactly like product-market fit, while actually representing the complete opposite.
So how do you spot these problems before you make a career-limiting decision? I've identified five critical warning signs that should make you think twice about any early-stage data or AI startup opportunity.
Warning sign number one: Employee count mismatch. If a database or infrastructure startup has more than 50 employees before they've proven sustainable unit economics, that's a massive red flag. Infrastructure companies can achieve enormous scale with small teams. PlanetScale handles massive enterprise workloads with under 40 employees. Neon had 130 people and couldn't turn a profit. When you see massive teams before massive revenue, you're looking at a company that either doesn't understand efficiency or is solving the wrong problem entirely.
Warning sign number two: Free tier economics that don't add up. Free tiers should be loss leaders that convert to profitable accounts. If the company can't articulate exactly how free users become profitable customers, or if conversion rates are below 5%, the business model is broken. Neon's generous free tier attracted hundreds of thousands of users, but the vast majority never converted to paying anything meaningful. The land and expand strategy only works when the land costs less than the expand revenue.
Warning sign number three: Usage growth disconnected from revenue growth. This is the big one. Impressive user adoption and usage metrics with flat or declining revenue per user should terrify you. In infrastructure tools, usage should correlate with willingness to pay. If users love the product but won't pay for it, you're not solving a valuable enough problem. This is especially dangerous when usage is driven by automated systems or AI tools rather than deliberate human decisions.
Warning sign number four: Fundraising history that suggests valuation inflation. Multiple large funding rounds with valuations that seem disconnected from revenue or clear path to profitability create impossible expectations. Inflated valuations make future fundraising difficult and create pressure for unsustainable growth tactics. When companies raise at billion-dollar valuations without billion-dollar revenue potential, they're setting up for down rounds or forced acquisitions.
Warning sign number five: Target markets that fundamentally resist paying for tools. Some markets, particularly individual developers and small teams, have strong cultural resistance to paying for infrastructure tools. If your primary market consists of users who expect everything to be free, you need a clear strategy for reaching different buyers with actual budgets. Neon's primary users were exactly these types of users, developers working on side projects and small teams who would never pay meaningful amounts for database hosting.
The Databricks acquisition reveals the harsh reality. When large companies acquire startups like this, they're not buying successful businesses, they're buying solutions to their own strategic problems. Databricks needed a way to capture smaller teams and projects before they scaled to enterprise size. Neon had built exactly that, even though they couldn't monetize it themselves.
For early employees, this can mean diluted equity, eliminated positions, and career disruption, even when the acquisition is positioned as a success story.
This doesn't mean avoiding all early-stage infrastructure startups. Instead, it means applying rigorous evaluation criteria that go beyond product elegance and founder charisma. Demand clear explanations of unit economics and path to profitability. Understand exactly who pays for the product and why. Evaluate whether the target market has a history of paying for similar solutions. Assess whether the team size and burn rate align with the business model complexity. Look for evidence that usage correlates with willingness to pay.
Most importantly, recognize that in infrastructure and AI tools, technical innovation alone is never sufficient. The combination of technical excellence and business model clarity is rare, but it's the only foundation for sustainable career growth.
The golden age of silicon that Jensen Huang describes requires not just breakthrough engineering, but breakthrough business models that can capture value from the innovation. As a sales professional, your success depends on joining companies that have figured out both sides of this equation. Don't let impressive technology and passionate founders blind you to fundamental business model problems. The cost of that mistake is measured not just in lost time, but in lost equity, lost opportunities, and potentially years of career setback.
Choose wisely.