Running out of money is a story as old as startups, and still highly relevant in 2026. According to recent findings of CB Insights, based on an analysis of 431 VC-backed companies that shut down since 2023, “ran out of capital” tops the list at 70%.
Yet, while burn is often treated as the core issue, the truth is it’s a symptom of something deeper: fragmented data, unclear priorities, a lack of visibility into what actually drives results, you name it. In this article, we’ll dig deeper into the core roots of it.
The hard truth about why founders operate in the dark
Scaling a company is grueling work: long hours, constant decisions, and the pressure to keep everything moving – product, hiring, sales, strategy, investments, you name it. High-stakes decisions every day, often without full visibility into what’s driving the business and the ripple effects those decisions create.
Under this constant pressure, founders often end up navigating without clear operational clarity. It shows up in subtle but compounding ways: problems are handled reactively rather than anticipated, issues only become visible once they’ve already impacted performance or budget, teams operate without a shared source of truth, and so on.
As a result, decisions are often made in silos without reliable metrics or a proper understanding of what’s truly driving results or scaling costs.
The 💜 of EU tech
The latest rumblings from the EU tech scene, a story from our wise ol’ founder Boris, and some questionable AI art. It’s free, every week, in your inbox. Sign up now!
However, in real-life business scenarios, operating in the dark is far more complex than that. It’s not just about merely missing data; it’s about fragmented systems, delayed feedback loops, and metrics that don’t connect across functions.
Financial, product, and operational signals often live in separate tools, which makes it difficult to trace cause and effect. For example, what looks like a growth problem may actually be a retention issue, or a cost spike may stem from architectural decisions made months earlier.
To start uncovering these bottlenecks, ask yourself:
- Where do we lack a single source of truth?
- Are any of our teams optimizing for different outcomes?
- Where are costs increasing without a clear driver?
- Which tools do potentially overlap without clear ownership?
- Does handoff friction slow our execution?
- Where are we scaling activity faster than we’re scaling efficiency?
Doing so will help you avoid a range of inefficiencies and misaligned decisions. Remember: a lack of clear visibility doesn’t just reduce efficiency, it amplifies risk across every layer of the business.
First, it distorts decision-making. When founders lack clear, reliable signals, decisions are driven by assumptions or bias, for example, doubling down on a feature because of a few vocal customer requests while ignoring data showing low overall adoption.
That often leads to doubling down on the wrong initiatives while underinvesting in what actually works.
Second, it quietly erodes margins. Costs don’t spike overnight, and more often accumulate unnoticed across redundant systems, idle resources, inefficient processes, or poorly aligned teams.
Moreover, a lack of clear spend visibility leads to poor strategic choices. Let’s explore how this happens and how to avoid it.
The impact of limited spend clarity: key tendencies
Without visibility into spend and returns, growth decisions are often based on assumptions rather than actual business needs.
Over time, this creates a false sense of progress. Metrics may look positive on the surface: growth, hiring, and feature velocity are all there.
However, without understanding the underlying drivers, that progress can be fragile and cascade into further consequences. Let’s review several business scenarios exemplifying that.
> Hiring to move faster
Teams often scale headcount to accelerate delivery and speed up growth. However, even when new hires are aligned with growth goals, leaders often fail to account for second-order effects (increased tooling costs, higher infrastructure usage, added collaboration overhead, more complex management layers that scale with the team, etc.)
In this case, watch out for metrics like revenue per employee, cost per feature/release, infrastructure cost per user or transaction, etc. – this way, you’ll not just be measuring how fast you’re growing, but whether that growth is actually improving efficiency and maintaining delivery quality.
> Scaling AI before proving ROI
The pressure to innovate is strong. However, in that push, AI initiatives are often expanded before their value is fully validated. Features are scaled to production or rolled out across users prematurely, which turns experimental costs into ongoing financial commitments.
To avoid this, companies should ensure they anchor every AI initiative to a clear business KPI, be it cost reduction, revenue uplift, time savings, or anything else. Always start with controlled pilots, not full rollouts.
Establish a cost baseline and track cost per inference / request. Also, solutions like LLM API can help you optimize costs by enabling you to auto-route your request to the most cost-efficient model, helping you avoid overpaying for simple tasks.
> Upgrading tools “for Later”
Another frequent cost driver among teams is investing in more advanced tooling earlier than necessary. This often stems from:
- Overestimating immediate requirements;
- Internal pressure to “scale fast”;
- Adopting tools based on trends rather than validated use cases;
- Lack of clear ownership over tooling decisions;
- Limited visibility into actual tool usage and ROI.
Whichever the reason, the outcome is the same: costs increase immediately without uncertain value, gradually reducing return on investment.
> Optimizing for flexibility in infrastructure
While flexibility and scalability enable rapid experimentation, they can come at a cost. Without proper cost governance, architectures on Amazon Web Services, Google Cloud Platform, or Azure often result in idle resources and steadily increasing expenses.
A smart way to offset these costs is by securing cloud credits – typically, cloud providers may offer up to $300,000 in credits for eligible fast-growing businesses.
The shift in perspective
When leaders gain a clear understanding of where money actually goes, across hiring, tooling, infrastructure, and operations, their behavior shifts from reactive execution to deliberate decision-making.
Instead of relying on assumptions or fragmented signals, they begin to connect actions with outcomes. This reduces the tendency to double down on misleading signals and replaces it with a more disciplined, outcome-driven approach.
This shift typically shows up in several ways:
> From reactive to proactive decisions. Issues are identified earlier, before they impact performance or budget. This, in turn, leads to more strategic actions and fewer downstream consequences.
> From assumptions to evidence-based thinking. When decisions are grounded in real drivers (not isolated signals or bias), leaders can prioritize what truly moves the business forward, and avoid investing in low-impact initiatives.
> From hidden inefficiencies to early detection. Cost accumulation across systems, teams, and workflows becomes visible and actionable, way before it impacts margins.
Ultimately, clarity over spend transforms leadership from navigating in the dark into operating with intent, with every decision being evaluated in the context of its broader business impact.
This shift is powerful not just because it reduces costs, but because it helps you understand and prevent them. Specifically, this is where platforms like Spendbase prove efficient – helping companies consolidate fragmented SaaS spend data and unlock hidden cost-saving opportunities.
Because, ultimately, the most effective founders are not those who spend the least,
but those who understand exactly why they spend, where it goes, and what it returns.
