Pivot Hell Is Real. Here's the Framework That Gets You Out.

Slack began as a struggling gaming studio before pivoting into a workplace communication tool. Instagram evolved from a cluttered social check-in app into a streamlined, photo-first experience. Shopify started as a niche snowboard retailer before transforming into a general-purpose e-commerce platform. In hindsight, those pivots seem inevitable and obvious. But as many founders know, navigating a pivot in real time is far from straightforward.

The lean startup framework urges entrepreneurs to form hypotheses, run experiments, gather customer feedback and pivot on negative signals. “But what happens when signals are positive yet divergent — or even contradictory?” says Darden Professor Saras Sarasvathy, an authority on the cognitive basis of high-performance entrepreneurship.

According to Sarasvathy, customer interviews, A/B tests, analytics, and investor input can all yield insights, yet often pull in different directions. Worse, they may surface new possibilities that demand further hypotheses and testing, ad infinitum. Ventures can become trapped in continuous adjustment, unable to distinguish real learning from noise.

“In practice, founders are often flooded with signals — customer feedback, investor advice, market data,” says Sarasvathy. “The problem is having no clear way to decide what is relevant.” 

As Sarasvathy argues, entrepreneurs can’t rely solely on hypothesis testing through data collection and analysis. They also need tools and mechanisms to help determine or create what is worth pursuing.

Sarasvathy offers such tools in a recent article, “Isotropy: Knowing when and how to Pivot,” forthcoming in The Journal of Business Venturing Insights and co-authored with Aman Bhuwania, assistant professor at the Goa Institute of Management in India.

The Real Problem Isn’t Lack of Information

Entrepreneurship is often framed as a problem of incomplete information — uncertain markets, unclear customer preferences and weak cause-and-effect signals. The default response is to gather more data. “But in modern startup environments, founders face an overload of inputs,” says Sarasvathy. “Too many signals — all plausible and often actionable, but they don’t cohere.”

Sarasvathy and Bhuwania call this “isotropy.” In entrepreneurial contexts, isotropy occurs when entrepreneurs face potentially meaningful signals, like customer feedback, investor advice, performance metrics and competitor signals, with no guidance to determine which information is relevant to their next action or strategy and which should be ignored or set aside for the time being.

When founders make decisions, they encounter two forms of isotropy. First, they struggle to determine the relevance of an overwhelming volume of input from diverse stakeholders — what Sarasvathy and her co-author term “signal isotropy.”

Second, founders face uncertainty about the stakeholders themselves: which of these potential actors will become committed partners — making their input worth incorporating — and which will not and can therefore be deprioritized. Sarasvathy refers to this as “stakeholder isotropy.”

“What’s troubling,” Sarasvathy notes, “is that founders often don’t even realize which potential stakeholders they’ve overlooked, simply because no one ever suggested engaging them.”

For novice founders, these problems are often invisible. They interpret confusion as a need for more data — when more data only deepens the confusion.

Nvidia: A Case Study in Navigating Noise

Nvidia is a great example of both forms of isotropy. The company invented the graphics processing units (GPUs) in the early 2000s, aiming to integrate them into every computer. Early on, Nvidia kept trying different business ideas based on predictions of what big customers and “the market” seemed to want.

In its early years, Nvidia repeatedly pursued promising opportunities — only to be derailed by shifting stakeholder dynamics. It built integrated graphics chips with AMD, only to be shut out when AMD acquired a competitor. It pursued partnerships with Intel, only to lose access when Intel’s priorities changed. It developed mobile computing solutions that excited Google but was blocked by Qualcomm’s control over critical components.

In each case, Nvidia had information. It had positive signals. It had validation from major players. What it lacked was a way to determine which signals were durable and relevant.

The problem wasn’t technical execution or market misunderstanding. It was isotropy: too many plausible paths shaped by stakeholders whose incentives kept shifting.

Nvidia’s breakthrough came when it stopped trying to interpret existing markets and instead focused on creating a new one. It stopped chasing feedback from current customers. Instead, the company decided to pursue AI and machine learning hardware — a “zero-billion-dollar market” where there were neither existing customers nor competitors, as Nvidia’s co-founder and CEO Jensen Huang put it.

By doing so, Nvidia effectively reduced isotropy. With fewer external signals, it could rely on internal conviction and controlled partnering. The lesson is not that founders should ignore markets. It’s that relevance is not discovered passively — it is constructed.

How Founders Fall Into “Pivot Hell”

When signals are abundant and conflicting, founders often end up in “pivot hell”— falling into three common traps:

1. Over-Pivoting  Every piece of feedback triggers a change in direction. The product, market, or strategy keeps shifting. The company never compounds learning because it never stays still long enough.

2. Analysis Paralysis Founders hesitate to act because signals don’t align. They wait for clarity that never arrives. Decision-making slows, and opportunities pass.

3. Misplaced Persistence Some founders ignore the noise and double down — but without a principled way to filter signals. They may commit to the wrong path, not out of conviction, but out of confusion.

In all three cases, the root issue is the same: the absence of mechanism to determine relevance.

A Better Approach: Generate Relevance, Don’t Just Collect Data

If more data isn’t the answer, what is?

As Sarasvathy and Bhuwania note, entrepreneurs should focus on generating relevance rather than gathering information, using effectuation-based principles such as affordable loss and stakeholder pre-commitments. These mechanisms act as filters that discipline attention and decision-making, helping founders determine when to pivot and when to stop.

The key shift is from information gathering to relevance generation. Instead of asking, “What does the data say?” founders should ask: “What makes this data matter?”

Sarasvathy and Bhuwania offer the following practical mechanisms:

Anchor Decisions in “Affordable Loss”

Rather than optimizing for expected returns — which requires reliable predictions — founders should define what they are willing to lose at each step. This concept — known as affordable loss — creates a natural endpoint to focus on relevance.

Founders should ask themselves: How much time will I spend testing a feature? How much capital will I allocate to a market experiment? How many iterations will I run before stopping?

By setting these constraints upfront, they reduce the need to interpret every signal. They are not trying to find the “right” answer — they are making progress within defined limits.

As Sarasvathy notes, “This shifts the question from ‘Is this the best opportunity?’ to ‘Is this worth pursuing within what I can afford to lose?’  Even more interestingly, it puts relevance front and center by asking ‘Is this worth pursuing even if I lose whatever I am willing to invest?’”

Prioritize Commitment Over Feedback

Not all signals are equal. The most reliable ones are those backed by commitments, notes Sarasvathy. A customer who says they like your product is offering feedback, while a customer who prepays, signs a contract, or integrates your solution is offering commitment.

The distinction is critical. Under isotropy, commitment is the strongest filter of relevance. Information accompanied by real stakes — money, time, reputation — carries far more weight than opinions.

Practically, this means shifting your questions:

Instead of: “Would you use this?”

Ask: “Would you pay for this now?”

Instead of: “What features do you want?”

Ask: “Which feature do you commit to using and selling this week?”

This approach reduces both kinds of isotropy by engaging only those stakeholders who are willing to commit through action, while sidelining those who merely talk.

Use “Pre-Commitments” to Guide Pivots

One of the hardest decisions founders face is when to pivot. Under isotropy, the rule from effectuation is simple but exacting: pivot only when a real commitment renders the new direction viable by bringing it within your affordable loss.

This standard enforces discipline. Feedback alone isn’t sufficient. Hypothetical demand doesn’t qualify. A pivot is justified only when a stakeholder — customer, partner, supplier or distributor  — commits in a way that materially supports the shift. The effect is a sharp reduction in unnecessary pivots. Each strategic change is anchored in demonstrated demand, not wishful speculation.

Create Your Own Filter: Values and Vision

In some cases, even commitment-backed signals are so noisy that they cannot be reliably interpreted.

Here, founders must rely on internal filters. One approach is what researchers call “redemptive choice” — using personal values or moral frameworks to prioritize certain paths over others. While this may sound abstract, it has practical implications: Which customers do you want to serve? Which problems are worth solving? And what trade-offs are you unwilling to make?

By clarifying these boundaries, founders reduce the range of relevant options—making decision-making more tractable.

When in Doubt, Reduce the Signal Environment

Nvidia’s ultimate move — entering a market with no clear customers — highlights a radical but effective strategy: reduce exposure to external noise. For early-stage founders, this might mean focusing on a narrow user segment, limiting the number of advisors, ignoring competitors in early stages and running fewer, more targeted experiments.

This is not about avoiding reality; it’s about controlling the information environment so that what is relevant can emerge.

The modern startup ecosystem celebrates speed, iteration and data-driven decision-making. But without mechanisms to determine relevance, these strengths become liabilities. Isotropy reminds us that clarity does not come from more signals — it comes from better filters.

For novice entrepreneurs, the path forward is not to listen more, but to listen more selectively and to engage in different kinds of conversations. As Sarasvathy puts it, “Focus less on what everyone is saying and more on who is willing to act and what you and they are willing to risk.”

Professor Saras  Sarasvathy is co-author of “Isotropy: Knowing when and how to Pivot,” forthcoming in The Journal of Business Venturing Insights, with Aman Bhuwania, assistant professor at the Goa Institute of Management in India (June 2026).